stability analysis and synthesis of nonlinear time delay systems via linear takagi sugeno fuzzy models abstract this paper concerns with the stability analysis and synthesis of nonlinear retarded systems via linear takagi sugeno ts fuzzy model approach first the ts fuzzy models with time delay are present and the stability conditions are derived using lyapunov razumikhin functional approach then a stabilization approach for nonlinear retarded systems through fuzzy state feedback and fuzzy observer based controller is proposed it shows that the analysis results provide an efficient technique for the design of fuzzy controllers sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain of the time delay systems are derived through the numerical solution of a set of coupled linear matrix inequalities

new delay dependent approach to robust stability and stabilization for takagi sugeno fuzzy time delay systems in this paper we consider new generalized delay dependent stability conditions of takagi sugeno fuzzy time delay systems in the literature both delay independent stability conditions and delay dependent stability conditions for fuzzy time delay systems have already been obtained however those conditions are rather conservative and do not guarantee a wide stability region this is also true in case of the robust stability for uncertain fuzzy time delay systems here we choose a generalized lyapunov functional and introduce a parameterized model transformation with free weighting matrices to it in order to obtain generalized delay dependent stability conditions in fact these techniques lead to generalized and less conservative stability conditions that guarantee a wide stability region our delay dependent stability conditions thus obtained are given in terms of linear matrix inequalities lmis we give three examples to illustrate our results comparison with other stability conditions in the literature shows our conditions are the most powerful ones to guarantee the widest stability region we also consider the robust stability of fuzzy time delay systems with uncertain parameters applying the same techniques obtained for the stability conditions we obtain delay dependent sufficient conditions for the robust stability of uncertain fuzzy systems moreover we give a design method of robustly stabilizing controllers for uncertain takagi sugeno fuzzy time delay systems

applications of cumulants to array processing iv direction finding in coherent signals case for pt iii see ibid vol 45 no 9 p 2253 64 1997 we present a subspace based direction finding method for coherent signal environments using an antenna array our method which uses fourth order statistics is capable of resolving more signals than a comparable second order statistics based subspace method and is applicable to a larger class of arrays the maximum number of signals resolvable with our method may exceed the number of sensors in the array only a uniform linear subarray is needed the rest of the array may have arbitrary and unknown response and does not require calibration on the other hand the comparable second order statistics based method is limited to uniform linear arrays only no search procedure is needed in our method simulation experiments supporting our conclusions are provided

impulsive delay differential inequality and stability of neural networks in this article a generalized model of neural networks involving time varying delays and impulses is considered by establishing the delay differential inequality with impulsive initial conditions and using the properties of m cone and eigenspace of the spectral radius of nonnegative matrices some new sufficient conditions for global exponential stability of impulsive delay model are obtained the results extend and improve the earlier publications an example is given to illustrate the theory

ridge regression biased estimation for nonorthogonal problems in multiple regression it is shown that parameter estimates based on minimum residual sum of squares have a high probability of being unsatisfactory if not incorrect if the prediction vectors are not orthogonal proposed is an estimation procedure based on adding small positive quantities to the diagonal of x x introduced is the ridge trace a method for showing in two dimensions the effects of nonorthogonality it is then shown how to augment x x to obtain biased estimates with smaller mean square error

statistical analysis of tls based prony techniques abstract we present an analysis of parameter variance statistics for the tls prony method applied to damped exponential signals we derive the covariance matrix of the estimated parameters for this method the parameters include the magnitudes and angles of the poles and the magnitudes and angles of the amplitude coefficients we verify the theoretical results using monte carlo simulations studies we also compare the variance results to the corresponding cramer rao bounds for several cases

shotgun stochastic search for large p regression model search in regression with very large numbers of candidate predictors raises challenges for both model specification and computation for which standard approaches such as markov chain monte carlo mcmc methods are often infeasible or ineffective we describe a novel shotgun stochastic search sss approach that explores interesting regions of the resulting high dimensional model spaces and quickly identifies regions of high posterior probability over models we describe algorithmic and modeling aspects priors over the model space that induce sparsity and parsimony over and above the traditional dimension penalization implicit in bayesian and likelihood analyses and parallel computation using cluster computers we discuss an example from gene expression cancer genomics comparisons with mcmc and other methods and theoretical and simulation based aspects of performance characteristics in large scale regression model searches we also provide software implementing the methods

an interference tolerant algorithm for localization of cyclostationary signal sources a method for detecting the number of cyclostationary signals radiated by remote sources and for estimating their directions of arrival by a linear and uniform array is presented whereas the traditional techniques exploit the spatial coherent properties the new method locates the signal sources using the spectral coherence properties as well this approach eliminates the need to know the characteristics of the noise and the interference regardless of the extent of their spectral overlap moreover the method applied equally well to environments containing more interferers than sensors the conditions of applicability of the method are the existence and the knowledge of a cycle frequency at which all the signal sources exhibit spectral correlation but the noise and interference signals do not and the existence and the knowledge of a value of the lag parameter such that the cyclic cross correlation matrix of the desired signals has full rank

multiple emitter location and signal parameter estimation processing the signals received on an array of sensors for the location of the emitter is of great enough interest to have been treated under many special case assumptions the general problem considers sensors with arbitrary locations and arbitrary directional characteristics gain phase polarization in a noise interference environment of arbitrary covariance matrix this report is concerned first with the multiple emitter aspect of this problem and second with the generality of solution a description is given of the multiple signal classification music algorithm which provides asymptotically unbiased estimates of 1 number of incident wavefronts present 2 directions of arrival doa or emitter locations 3 strengths and cross correlations among the incident waveforms 4 noise interference strength examples and comparisons with methods based on maximum likelihood ml and maximum entropy me as well as conventional beamforming are included an example of its use as a multiple frequency estimator operating on time series is included

statistical tests for presence of cyclostationarity the presence of kth order cyclostationarity is defined in terms of nonvanishing cyclic cumulants or polyspectra relying upon the asymptotic normality and consistency of kth order cyclic statistics asymptotically optimal spl chi sup 2 tests are developed to detect the presence of cycles in the kth order cyclic cumulants or polyspectra without assuming any specific distribution on the data constant false alarm rate tests are derived in both time and frequency domain and yield consistent estimates of possible cycles present in the kth order cyclic statistics explicit algorithms for k spl les 4 are discussed existing approaches are rather empirical and deal only with k spl les 2 case simulation results are presented to confirm the performance of the given tests

stability of fuzzy control systems with bounded uncertain delays global exponential stability of fuzzy control systems with delays is studied these delays in the fuzzy control systems are assumed to be any uncertain bounded continuous functions stability of systems with uncertain delays is interesting since in practical applications it is not easy to know the delays exactly conditions for global exponential stability of free fuzzy systems with uncertain delays are derived criteria for design of nonlinear fuzzy controllers to feedback control the stability of global nonlinear fuzzy systems are given theorems are proved via the method of functional differential inequalities analysis

the stability analysis of abstract takagi sugeno fuzzy impulsive system abstract in this paper based on the comparison method the sufficient conditions of stability in terms of the two measures of abstract locally linear impulsive differential equation with unbounded operator coefficients an abstract takagi sugeno fuzzy impulsive system have been derived as an application of the obtained results the locally linear impulsive system hybrid t s fuzzy system has been considered an example of impulsive nonperiodic finite dimension t s fuzzy system with unstable structural matrices was constructed it is shown that zero solution of such t s fuzzy system could be asymptotically stable in cone the simulations results of example demonstrate the effectiveness of proposed approach

on exponential stability results for fuzzy impulsive neural networks complex nonlinear systems can be represented to a set of linear sub models by using fuzzy sets and fuzzy reasoning via ordinary takagi sugeno ts fuzzy models in this paper the exponential stability of ts fuzzy neural networks with impulsive effect and time varying delays is investigated the model for fuzzy impulsive neural networks with time varying delays is first established as a modified ts fuzzy model in which the consequent parts are composed of a set of impulsive neural networks with time varying delays secondly the exponential stability for fuzzy impulsive neural networks is presented by utilizing the lyapunov krasovskii functional and the linear matrix inequality lmi approach in addition two numerical examples are provided to illustrate the applicability of the result using lmi control toolbox in matlab

output feedback robust h sup spl infin control of uncertain fuzzy dynamic systems with time varying delay this paper presents an output feedback robust h sup spl infin control problem for a class of uncertain fuzzy dynamic systems with time varying delayed state the takagi sugeno fuzzy model is employed to represent an uncertain nonlinear systems with time varying delayed state using a single quadratic lyapunov function the globally exponential stability and disturbance attenuation of the closed loop fuzzy control system are discussed sufficient conditions for the existence of robust h sup spl infin controllers are given in terms of matrix inequalities constructive algorithm for design of robust h sup spl infin controller is also developed the resulting controller is nonlinear and automatically tuned based on fuzzy operation

robust asymptotic stability of uncertain fuzzy bam neural networks with time varying delays via ordinary takagi sugeno ts fuzzy models complex nonlinear systems can be represented to a set of linear sub models by using fuzzy sets and fuzzy reasoning in this paper the global asymptotic stability problem of ts fuzzy bi directional associative memories bam neural networks with time varying delays and parameter uncertainties is considered first the model of ts fuzzy bam neural networks with time varying delays and parameter uncertainties is established as a modified ts fuzzy model in which the consequent parts are composed of a set of bam neural networks with time varying delays secondly the globally robust asymptotically stable condition is presented in terms of linear matrix inequalities which can be easily solved by some standard numerical packages two numerical examples are also given to validate the theoretical results

on the adaptive elastic net with a diverging number of parameters we consider the problem of model selection and estimation in situations where the number of parameters diverges with the sample size when the dimension is high an ideal method should have the oracle property fan and li 2001 fan and peng 2004 which ensures the optimal large sample performance furthermore the high dimensionality often induces the collinearity problem which should be properly handled by the ideal method many existing variable selection methods fail to achieve both goals simultaneously in this paper we propose the adaptive elastic net that combines the strengths of the quadratic regularization and the adaptively weighted lasso shrinkage under weak regularity conditions we establish the oracle property of the adaptive elastic net we show by simulations that the adaptive elastic net deals with the collinearity problem better than the other oracle like methods thus enjoying much improved finite sample performance

how useful is bagging in forecasting economic time series a case study of u s consumer price inflation this article focuses on the widely studied question of whether the inclusion of indicators of real economic activity lowers the prediction mean squared error of forecasting models of u s consumer price inflation we propose three variants of the bagging algorithm specifically designed for this type of forecasting problem and evaluate their empirical performance although bagging predictors in our application are clearly more accurate than equally weighted forecasts median forecasts arm forecasts after forecasts or bayesian forecast averages based on one extra predictor at a time they are generally about as accurate as the bayesian shrinkage predictor the ridge regression predictor the iterated lasso predictor or the bayesian model average predictor based on random subsets of extra predictors our results show that bagging can achieve large reductions in prediction mean squared errors even in such challenging applications as inflation forecasting however bagging is not the only method capable of

efficient subspace based algorithm for adaptive bearing estimation and tracking in some practical applications of array processing the directions of the incident signals should be estimated adaptively and or the time varying directions should be tracked promptly in this paper an adaptive bearing estimation and tracking abest algorithm is investigated for estimating and tracking the uncorrelated and correlated narrow band signals impinging on a uniform linear array ula based on the subspace based method without eigendecomposition sumwe where a linear operator is obtained from the array data to form a basis for the null space by exploiting the array geometry and its shift invariance property specifically the null space is estimated using the least mean square lms or normalized lms nlms algorithm and the directions are updated using the approximate newton method the transient analyses of the lms and nlms algorithms are studied where the weight i e the linear operator is in the form of a matrix and there is a correlation between the additive noise and input data that involve the instantaneous correlations of the received array data in the updating equation and the step size stability conditions are derived explicitly in addition the analytical expressions for the mean square error mse and mean square deviation msd learning curves of the lms algorithm are clarified the effectiveness of the abest algorithm is verified and the theoretical analyses are corroborated through numerical examples simulation results show that the abest algorithm is computationally simple and has good adaptation and tracking abilities

computing bayes factors by combining simulation and asymptotic approximations abstract the bayes factor is a ratio of two posterior normalizing constants which may be difficult to compute we compare several methods of estimating bayes factors when it is possible to simulate observations from the posterior distributions via markov chain monte carlo or other techniques the methods that we study are all easily applied without consideration of special features of the problem provided that each posterior distribution is well behaved in the sense of having a single dominant mode we consider a simulated version of laplace s method a simulated version of bartlett correction importance sampling and a reciprocal importance sampling technique we also introduce local volume corrections for each of these in addition we apply the bridge sampling method of meng and wong we find that a simulated version of laplace s method with local volume correction furnishes an accurate approximation that is especially useful when likelihood function evaluations are costly a simple bridge sampli

esprit estimation of signal parameters via rotational invariance techniques an approach to the general problem of signal parameter estimation is described the algorithm differs from its predecessor in that a total least squares rather than a standard least squares criterion is used although discussed in the context of direction of arrival estimation esprit can be applied to a wide variety of problems including accurate detection and estimation of sinusoids in noise it exploits an underlying rotational invariance among signal subspaces induced by an array of sensors with a translational invariance structure the technique when applicable manifests significant performance and computational advantages over previous algorithms such as mem capon s mlm and music

forward backward spatial smoothing techniques for coherent signal identification a spatial smoothing scheme is further investigated in the context of coherent signal classification it is shown that by making use of a set of forward and complex conjugated backward subarrays simultaneously it is always possible to estimate any k directions of arrival using at most 3k 2 sensor elements this is achieved by creating a smoothed array output covariance matrix that is structurally identical to a covariance matrix in some noncoherent situation by incorporating eigenstructure based techniques on this smoothed covariance matrix it then becomes possible to correctly identify all directions of arrival irrespective of their correlation

forecasting using random subspace methods textabstractrandom subspace methods are a novel approach to obtain accurate forecasts in high dimensional regression settings we provide a theoretical justification of the use of random subspace methods and show their usefulness when forecasting monthly macroeconomic variables we focus on two approaches the first is random subset regression where random subsets of predictors are used to construct a forecast second we discuss random projection regression where artificial predictors are formed by randomly weighting the original predictors using recent results from random matrix theory we obtain a tight bound on the mean squared forecast error for both randomized methods we identify settings in which one randomized method results in more precise forecasts than the other and than alternative regularization strategies such as principal component regression partial least squares lasso and ridge regression the predictive accuracy on the high dimensional macroeconomic fred md data set increases substantially when using the randomized methods with random subset regression outperforming any one of the above mentioned competing methods for at least 66 of the series

sampling based approaches to calculating marginal densities abstract stochastic substitution the gibbs sampler and the sampling importance resampling algorithm can be viewed as three alternative sampling or monte carlo based approaches to the calculation of numerical estimates of marginal probability distributions the three approaches will be reviewed compared and contrasted in relation to various joint probability structures frequently encountered in applications in particular the relevance of the approaches to calculating bayesian posterior densities for a variety of structured models will be discussed and illustrated

bayesian model averaging for linear regression models abstract we consider the problem of accounting for model uncertainty in linear regression models conditioning on a single selected model ignores model uncertainty and thus leads to the underestimation of uncertainty when making inferences about quantities of interest a bayesian solution to this problem involves averaging over all possible models i e combinations of predictors when making inferences about quantities of interest this approach is often not practical in this article we offer two alternative approaches first we describe an ad hoc procedure occam s window which indicates a small set of models over which a model average can be computed second we describe a markov chain monte carlo approach that directly approximates the exact solution in the presence of model uncertainty both of these model averaging procedures provide better predictive performance than any single model that might reasonably have been selected in the extreme case where there are many candidate predictors but

performance of high resolution frequencies estimation methods compared to the cramer rao bounds an explicit expression for the cramer rao bounds crbs and a calculation of the statistical perturbation of the covariance matrix due to additive noise are presented the results are applied to a statistical efficiency analysis of the main frequency estimation methods based on eigenvalue decomposition for the covariance matrix in order to characterize the perturbation of the signal subspace only the component of the perturbation of the eigenvectors orthogonal to the subspace is considered this gives a simpler and more significant form of the error covariance the treatment includes the cases of forward backward and moving averages the crb and estimation variances are calculated in the presence of additive random noise but for a given set of amplitudes characterized by their sample covariance matrix this approach is more realistic for the evaluation of efficiency in the small sample case

simple and efficient nonparametric method for estimating the number of signals without eigendecomposition inspired by the computational simplicity and numerical stability of qr decomposition a nonparametric method for estimating the number of signals without eigendecomposition mense is proposed for the coherent narrowband signals impinging on a uniform linear array ula by exploiting the array geometry and its shift invariance property to decorrelate the coherency of signals through subarray averaging the number of signals is revealed in the rank of the qr upper trapezoidal factor of the autoproduct of a combined hankel matrix formed from the cross correlations between some sensor data since the infection of additive noise is defused signal detection capability is improved a new detection criterion is then formulated in terms of the row elements of the qr upper triangular factor when finite array data are available and the number of signals is determined as a value of the running index for which this ratio criterion is maximized where the qr decomposition with column pivoting is also used to improve detection performance the statistical analysis clarifies that the mense detection criterion is asymptotically consistent furthermore the proposed mense algorithm is robust against the array uncertainties including sensor gain and phase errors and mutual coupling and against the deviations from the spatial homogeneity of noise model the effectiveness of the mense is verified through numerical examples and the simulation results show that the mense is superior in detecting closely spaced signals with a small number of snapshots and or at relatively low signal to noise ratio snr

the statistical performance of the music and the minimum norm algorithms in resolving plane waves in noise this paper presents an asymptotic statistical analysis of the null spectra of two eigen assisted methods music 1 and minimum norm 2 for resolving independent closely spaced plane waves in noise particular attention is paid to the average deviation of the null spectra from zero at the true angles of arrival for the plane waves these deviations are expressed as functions of signal to noise ratios number of array elements angular separation of emitters and the number of snapshots in the case of music an approximate expression is derived for the resolution threshold of two plane waves with equal power in noise this result is validated by monte carlo simulations

efficient estimation of covariance selection models summary a bayesian method is proposed for estimating an inverse covariance matrix from gaussian data the method is based on a prior that allows the off diagonal elements of the inverse covariance matrix to be zero and in many applications results in a parsimonious parameterisation of the covariance matrix no assumption is made about the structure of the corresponding graphical model so the method applies to both nondecomposable and decomposable graphs all the parameters are estimated by model averaging using an efficient metropolis hastings sampling scheme a simulation study demonstrates that the method produces statistically efficient estimators of the covariance matrix when the inverse covariance matrix is sparse the methodology is illustrated by applying it to three examples that are high dimensional relative to the sample size

delay dependent stability analysis and control synthesis of fuzzy dynamic systems with time delay this paper focuses on the delay dependent stability analysis and stabilization for fuzzy control systems with time delay based on linear matrix inequality lmi approach a delay dependent stability criteria has been developed the design schemes of stabilization via state feedback is developed and then the result is extended to the case of observer based output feedback all the researching results are presented by means of lmis two illustrative examples are given to illustrate the validity of the proposed design procedures

impulsive control for existence uniqueness and global stability of periodic solutions of recurrent neural networks with discrete and continuously distributed delays in this paper a class of recurrent neural networks with discrete and continuously distributed delays is considered sufficient conditions for the existence uniqueness and global exponential stability of a periodic solution are obtained by using contraction mapping theorem and stability theory on impulsive functional differential equations the proposed method which differs from the existing results in the literature shows that network models may admit a periodic solution which is globally exponentially stable via proper impulsive control strategies even if it is originally unstable or divergent two numerical examples and their computer simulations are offered to show the effectiveness of our new results

two dimensional direction estimation for a mixture of noncoherent and coherent signals this paper deals with the two dimensional 2 d direction of arrival doa estimation of a mixture of noncoherent including uncorrelated and partially correlated and coherent i e fully correlated narrowband signals impinging on a planar sensor array composed of two parallel uniform linear arrays ulas an oblique projection based approach for 2 d direction estimation opade is proposed by using some cross correlations between the received array data in the proposed opade the oblique projection is utilized to isolate the coherent signals from the noncoherent ones and the effect of additive noise is alleviated while the computationally intensive eigendecomposition is avoided and the estimated elevation and azimuth angles are paired automatically further an iterative alternating scheme is presented to improve the estimation accuracy of the oblique projector and hence that of the doas of coherent signals the cramer rao lower bound crb for the mixture of noncoherent and coherent signals is also derived explicitly where the prior knowledge of the signal correlation is incorporated into the 2 d doa estimation of noncoherent signals finally the effectiveness of the opade and the theoretical analysis are substantiated through numerical examples

global exponential p stability of stochastic non autonomous takagi sugeno fuzzy cellular neural networks with time varying delays and impulses in this paper we investigate the global exponential p stability of stochastic non autonomous takagi sugeno fuzzy cellular neural networks with time varying delays and impulses by using the property of m matrix we establish a new differential inequality and a new l operator inequality to be effective for non autonomous takagi sugeno fuzzy systems based on the inequalities obtained above sufficient conditions ensuring the global exponential p stability of the considered system are obtained in addition we derive sufficient conditions ensuring the global exponential stability of the corresponding autonomous systems our results improve some early results in the literature one example is given to illustrate the effectiveness of our proposal

mixtures of g priors for bayesian model averaging with economic application this paper examines the issue of variable selection in linear regression modeling where there is a potentially large amount of possible covariates and economic theory offers insufficient guidance on how to select the appropriate subset in this context bayesian model averaging presents a formal bayesian solution to dealing with model uncertainty the main interest here is the effect of the prior on the results such as posterior inclusion probabilities of regressors and predictive performance the authors combine a binomial beta prior on model size with a g prior on the coefficients of each model in addition they assign a hyperprior to g as the choice of g has been found to have a large impact on the results for the prior on g they examine the zellner siow prior and a class of beta shrinkage priors which covers most choices in the recent literature the authors propose a benchmark beta prior inspired by earlier findings with fixed g and show it leads to consistent model selection inference is conducted through a markov chain monte carlo sampler over model space and g the authors examine the performance of the various priors in the context of simulated and real data for the latter they consider two important applications in economics namely cross country growth regression and returns to schooling recommendations for applied users are provided

calibration and empirical bayes variable selection for the problem of variable selection for the normal linear model selection criteria such as aic c p bic and ric have fixed dimensionality penalties such criteria are shown to correspond to selection of maximum posterior models under implicit hyperparameter choices for a particular hierarchical bayes formulation based on this calibration we propose empirical bayes selection criteria that use hyperparameter estimates instead of fixed choices for obtaining these estimates both marginal and conditional maximum likelihood methods are considered as opposed to traditional fixed penalty criteria these empirical bayes criteria have dimensionality penalties that depend on the data their performance is seen to approximate adaptively the performance of the best fixed penalty criterion across a variety of orthogonal and nonorthogonal set ups including wavelet regression empirical bayes shrinkage estimators of the selected coefficients are also proposed

signal selective localization of nongaussian cyclostationary sources novel direction finding algorithms that exploit the nongaussian and cyclostationary nature of communication signals are explored the proposed methods that are appropriate for uniform linear arrays employ cyclic higher order statistics of the array output and suppress additive gaussian noise of unknown spectral content even when the noise shares common cycle frequencies with the nongaussian signals of interest in addition cyclic higher order statistics are tolerant to nongaussian interferences with cycle frequencies other than those of the desired signals and allow one to consistently estimate the angles of arrival of signal sources per cycle whose number can be greater than the number of sensors

optimal prediction pools a prediction model is any statement of a probability distribution for an outcome not yet observed this study considers the properties of weighted linear combinations of n prediction models or linear pools evaluated using the conventional log predictive scoring rule the log score is a concave function of the weights and in general an optimal linear combination will include several models with positive weights despite the fact that exactly one model has limiting posterior probability one the paper derives several interesting formal results for example a prediction model with positive weight in a pool may have zero weight if some other models are deleted from that pool the results are illustrated using s p 500 returns with prediction models from the arch stochastic volatility and markov mixture families in this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools and these pools substantially outperform their best components

stability of the zero solution of impulsive differential equations by the lyapunov second method abstract the paper is concerned with the stability of the zero solution of the impulsive system dx dt f t x t i x x t i x j i x i n 1 2 where x t x x x limt x t the lyapunov second method is used as a tool in obtaining the criteria for stability asymptotic stability and instability of the trivial solution

singular value decomposition and improved frequency estimation using linear prediction linear prediction based lp methods for fitting multiple sinusoid signal models to observed data such as the forward backward fblp method of nuttall 5 and ulrych and clayton 6 are very ill conditioned the locations of estimated spectral peaks can be greatly affected by a small amount of noise because of the appearance of outliers lp estimation of frequencies can be greatly improved at low snr by singular value decomposition svd of the lp data matrix the improved performance at low snr is also better than that obtained by using the eigenvector corresponding to the minimum eigenvalue of the correlation matrix as is done in pisarenko s method and its variants

benchmark priors for bayesian model averaging in contrast to a posterior analysis given a particular sampling model posterior model probabilities in the context of model uncertainty are typically rather sensitive to the speciflcation of the prior in particular difiuse priors on model speciflc parameters can lead to quite unexpected consequences here we focus on the practically relevant situation where we need to entertain a large number of sampling models and we have or wish to use little or no subjective prior information we aim at providing an automatic or benchmark prior structure that can be used in such cases we focus on the normal linear regression model with uncertainty in the choice of re gressors we propose a partly noninformative prior structure related to a natural conjugate g prior speciflcation where the amount of subjective information requested from the user is limited to the choice of a single scalar hyperparameter g0j the consequences of difierent choices for g0j are examined we investigate theoretical properties such as consistency of the implied bayesian procedure links with classical information criteria are provided in addition we examine the flnite sample implications of several choices of g0j in a simulation study the use of the mc 3 algorithm of madigan and york 1995 combined with e cient coding in fortran makes it feasible to conduct large simulations in addition to posterior criteria we shall also compare the predictive performance of difierent priors a classic exam ple concerning the economics of crime will also be provided and contrasted with results in the literature the main flndings of the paper will lead us to propose a benchmark prior speciflcation in a linear regression context with model uncertainty

delayed standard neural network models for control systems in order to conveniently analyze the stability of recurrent neural networks rnns and successfully synthesize the controllers for nonlinear systems similar to the nominal model in linear robust control theory the novel neural network model named delayed standard neural network model dsnnm is presented which is the interconnection of a linear dynamic system and a bounded static delayed or nondelayed nonlinear operator by combining a number of different lyapunov functionals with s procedure some useful criteria of global asymptotic stability and global exponential stability for the continuous time dsnnms cdsnnms and discrete time dsnnms ddsnnms are derived whose conditions are formulated as linear matrix inequalities lmis based on the stability analysis some state feedback control laws for the dsnnm with input and output are designed to stabilize the closed loop systems most rnns and neurocontrol nonlinear systems with or without time delays can be transformed into the dsnnms to be stability analyzed or stabilization synthesized in a unified way in this paper the dsnnms are applied to analyzing the stability of the continuous time and discrete time rnns with or without time delays and synthesizing the state feedback controllers for the chaotic neural network system and discrete time nonlinear system it turns out that the dsnnm makes the stability conditions of the rnns easily verified and provides a new idea for the synthesis of the controllers for the nonlinear systems

perturbation analysis of an svd based linear prediction method for estimating the frequencies of multiple sinusoids a linear prediction approach is studied for estimating the frequencies of sinusoids in white noise it is shown that in the first step the continuity of the generalized inverse and the concept of angle between subspaces play an important role the continuity concept helps explain the need for a low rank approximation and the quality of the approximation is appraised by using the notion of angle between subspaces for the second step the sensitivity of the zeros of the predictor polynomial becomes an important consideration and is examined it is shown that increasing the order of the predictor polynomial and computing the minimum norm solution provides a mechanism to reduce parameter sensitivity

maximum likelihood localization of multiple sources by alternating projection an algorithm referred to as apm for computing the maximum likelihood estimator of the locations of simple sources in passive sensor arrays is presented the algorithm is equally applicable to the case of coherent signals and to the case of a single snapshot the algorithm is iterative the maximum of the likelihood function is computed by successive approximations the convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios the key to this global convergence is the initialization scheme

complete subset regressions abstract this paper proposes a new method for combining forecasts based on complete subset regressions for a given set of potential predictor variables we combine forecasts from all possible linear regression models that keep the number of predictors fixed we explore how the choice of model complexity as measured by the number of included predictor variables can be used to trade off the bias and variance of the forecast errors generating a setup akin to the efficient frontier known from modern portfolio theory in an application to predictability of stock returns we find that combinations of subset regressions can produce more accurate forecasts than conventional approaches based on equal weighted forecasts which fail to account for the dimensionality of the underlying models combinations of univariate forecasts or forecasts generated by methods such as bagging ridge regression or bayesian model averaging

forecasting with factor augmented regression a frequentist model averaging approach abstract this paper considers forecast combination with factor augmented regression in this framework a large number of forecasting models are available varying by the choice of factors and the number of lags we investigate forecast combination across models using weights that minimize the mallows and the leave h out cross validation criteria the unobserved factor regressors are estimated by principle components of a large panel with n predictors over t periods with these generated regressors we show that the mallows and leave h out cross validation criteria are asymptotically unbiased estimators of the one step ahead and multi step ahead mean squared forecast errors respectively provided that n t however the paper does not establish any optimality properties for the methods in contrast to well known results in the literature this result suggests that the generated regressor issue can be ignored for forecast combination without restrictions on the relation between n and t simulations show that the mallows model averaging and leave h out cross validation averaging methods yield lower mean squared forecast errors than alternative model selection and averaging methods such as aic bic cross validation and bayesian model averaging we apply the proposed methods to the us macroeconomic data set in stock and watson 2012 and find that they compare favorably to many popular shrinkage type forecasting methods

computationally efficient subspace based method for direction of arrival estimation without eigendecomposition a computationally simple direction of arrival doa estimation method with good statistical performance is attractive in many practical applications of array processing in this paper we propose a new computationally efficient subspace based method without eigendecomposition sumwe for the coherent narrowband signals impinging on a uniform linear array ula by exploiting the array geometry and its shift invariance property the coherency of incident signals is decorrelated through subarray averaging and the null space is obtained through a linear operation of a matrix formed from the cross correlations between some sensor data where the effect of additive noise is eliminated consequently the doas can be estimated without performing eigendecomposition and there is no need to evaluate all correlations of the array data furthermore the sumwe is also suitable for the case of partly coherent or incoherent signals and it can be extended to the spatially correlated noise by choosing appropriate subarrays the statistical analysis of the sumwe is studied and the asymptotic mean squared error mse expression of the estimation error is derived the performance of the sumwe is demonstrated and the theoretical analysis is substantiated through numerical examples it is shown that the sumwe is superior in resolving closely spaced coherent signals with a small number of snapshots and at low signal to noise ratio snr and offers good estimation performance for both uncorrelated and correlated incident signals

boundedness of the solutions of impulsive differential systems with time varying delay in this paper we investigate boundedness of the solutions of impulsive functional differential equations with time varying delay by using lyapunov functions and razumikhin techniques we will see that impulses do contribute to the solutions boundedness property

delay dependent lmi conditions for stability and stabilization of t s fuzzy systems with bounded time delay this paper studies the stability and stabilization problems for time delay nonlinear systems through takagi sugeno t s fuzzy model approach the time delays are assumed to be bounded but perhaps non smooth delay dependent conditions are presented in terms of lmis illustrative examples are given to show the effectiveness and merits of the present results

a bayesian approach to nonparametric bivariate regression abstract this article outlines a general bayesian approach to estimating a bivariate regression function in a nonparametric manner it models the function using a bivariate regression spline basis with many terms binary indicator variables corresponding to these terms are introduced to explicitly model the uncertainty of whether or not the terms provide a significant contribution to the regression the regression function is estimated using an estimate of its posterior mean smoothing over the distribution of these binary indicator variables to make the computations tractable all estimates are obtained using markov chain monte carlo sampling extensive simulated comparisons are provided that demonstrate the competitive performance of this approach against other data driven bivariate surface estimators prominent in the literature it is then shown how the procedure can be extended to provide a general approach to nonparametric bivariate surface estimation in two difficult regression settings the first

bayesian cart model search abstract in this article we put forward a bayesian approach for finding classification and regression tree cart models the two basic components of this approach consist of prior specification and stochastic search the basic idea is to have the prior induce a posterior distribution that will guide the stochastic search toward more promising cart models as the search proceeds such models can then be selected with a variety of criteria such as posterior probability marginal likelihood residual sum of squares or misclassification rates examples are used to illustrate the potential superiority of this approach over alternative methods

total least squares approach for frequency estimation using linear prediction the resolution of the estimated closely spaced frequencies of the multiple sinusoids degrades as the signal to noise ratio snr of the received signal becomes low this resolution can be improved by using the total least squares tls method in solving the linear prediction lp equation this approach makes use of the singular value decomposition svd of the augmented matrix for low rank approximation to reduce the noise effect from both the observation vector and the lp data matrix simultaneously comparison is made to the principle eigenvector pe method of tufts and kumaresan both on theoretical and experimental grounds the tls algorithm exhibits superior performance over the pe method where low rank approximation is applied to the data matrix only

analysis and synthesis of nonlinear time delay systems via fuzzy control approach takagi sugeno ts fuzzy models 1985 1992 can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input output i o submodels in this paper the ts fuzzy model approach is extended to the stability analysis and control design for both continuous and discrete time nonlinear systems with time delay the ts fuzzy models with time delay are presented and the stability conditions are derived using lyapunov krasovskii approach we also present a stabilization approach for nonlinear time delay systems through fuzzy state feedback and fuzzy observer based controller sufficient conditions for the existence of fuzzy state feedback gain and fuzzy observer gain are derived through the numerical solution of a set of coupled linear matrix inequalities an illustrative example based on the cstr model is given to design a fuzzy controller

distribution theory of the least squares averaging estimator this paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework we show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug in averaging estimator that minimizes the sample analog of the asymptotic mean squared error we investigate the focused information criterion claeskens and hjort 2003 the plug in averaging estimator the mallows model averaging estimator hansen 2007 and the jackknife model averaging estimator hansen and racine 2012 we find that the asymptotic distributions of averaging estimators with data dependent weights are nonstandard and cannot be approximated by simulation to address this issue we propose a simple procedure to construct valid confidence intervals with improved coverage probability monte carlo simulations show that the plug in averaging estimator generally has smaller expected squared error than other existing model averaging methods and the coverage probability of proposed confidence intervals achieves the nominal level as an empirical illustration the proposed methodology is applied to cross country growth regressions

comparative study of iqml and mode direction of arrival estimators we present a comparative study of using the iterative quadratic maximum likelihood iqml and the method of direction estimation mode for direction of arrival doa estimation of narrowband incident signals with a uniform linear array the conditions for local convergence and the theoretical performance of iqml are derived although the doa estimates obtained via mode are asymptotically statistically efficient we show that those obtained via iqml are almost always inconsistent and have much larger mean squared errors we also show that the amount of computations required by iqml is usually much larger than that required by mode especially for low signal to noise ratio snr and a large number of snapshots

optimal weight choice for frequentist model average estimators there has been increasing interest recently in model averaging within the frequentist paradigm the main benefit of model averaging over model selection is that it incorporates rather than ignores the uncertainty inherent in the model selection process one of the most important yet challenging aspects of model averaging is how to optimally combine estimates from different models in this work we suggest a procedure of weight choice for frequentist model average estimators that exhibits optimality properties with respect to the estimator s mean squared error mse as a basis for demonstrating our idea we consider averaging over a sequence of linear regression models building on this base we develop a model weighting mechanism that involves minimizing the trace of an unbiased estimator of the model average estimator s mse we further obtain results that reflect the finite sample as well as asymptotic optimality of the proposed mechanism a monte carlo study based on simulated and real data evaluates

bayesian variable selection in clustering high dimensional data over the last decade technological advances have generated an explosion of data with substantially smaller sample size relative to the number of covariates p n a common goal in the analysis of such data involves uncovering the group structure of the observations and identifying the discriminating variables in this article we propose a methodology for addressing these problems simultaneously given a set of variables we formulate the clustering problem in terms of a multivariate normal mixture model with an unknown number of components and use the reversible jump markov chain monte carlo technique to define a sampler that moves between different dimensional spaces we handle the problem of selecting a few predictors among the prohibitively vast number of variable subsets by introducing a binary exclusion inclusion latent vector which gets updated via stochastic search techniques we specify conjugate priors and exploit the conjugacy by integrating out some of the parameters we describe strategies

music maximum likelihood and cramer rao bound the performance of the music and ml methods is studied and their statistical efficiency is analyzed the cramer rao bound crb for the estimation problems is derived and some useful properties of the crb covariance matrix are established the relationship between the music and ml estimators is investigated as well a numerical study is reported of the statistical efficiency of the music estimator for the problem of finding the directions of two plane waves using a uniform linear array an exact description of the results is included

approximately normal tests for equal predictive accuracy in nested models abstract forecast evaluation often compares a parsimonious null model to a larger model that nests the null model under the null that the parsimonious model generates the data the larger model introduces noise into its forecasts by estimating parameters whose population values are zero we observe that the mean squared prediction error mspe from the parsimonious model is therefore expected to be smaller than that of the larger model we describe how to adjust mspes to account for this noise we propose applying standard methods west k d 1996 asymptotic inference about predictive ability econometrica 64 1067 1084 to test whether the adjusted mean squared error difference is zero we refer to nonstandard limiting distributions derived in clark and mccracken 2001 tests of equal forecast accuracy and encompassing for nested models journal of econometrics 105 85 110 2005a evaluating direct multistep forecasts econometric reviews 24 369 404 to argue that use of standard normal critical values will yield actual sizes close to but a little less than nominal size simulation evidence supports our recommended procedure

robust stability of uncertain fuzzy bam neural networks of neutral type with markovian jumping parameters and impulses abstract in this paper the problem of neutral type impulsive bidirectional associative memory neural networks nibamnns with time delays are first established by a takagi sugeno t s fuzzy model in which the consequent parts are composed of a set of nibamnns with interval delays and markovian jumping parameters mjps sufficient conditions to check the robust exponential stability of the derived model are based on the lyapunov krasovskii functionals lkfs containing some novel triple integral terms lyapunov stability theory and employing the free weighting matrix method the delay dependent stability conditions are established in terms of linear matrix inequalities lmis which can be very efficiently solved using matlab lmi control toolbox finally numerical examples and remarks are given to illustrate the effectiveness and usefulness of the derived results

robust stability and stabilization for uncertain takagi sugeno fuzzy time delay systems in this paper we consider robust stability and stabilization of uncertain takagi sugeno fuzzy time delay systems where uncertainties come into the state and input matrices some stability conditions and robust stability conditions for fuzzy time delay systems have already been obtained in the literature however those conditions are rather conservative and do not guarantee the stability and robust stability for a wide class of fuzzy systems this is true in case of designing stabilizing controllers for fuzzy time delay systems we first consider robust stability conditions of uncertain fuzzy systems conditions we obtain here are delay dependent conditions that depend on the upper bound of time delay and are given in linear matrix inequalities lmis an appropriate selection of lyapunov krasovskii function and introduction of free weighting matrices generalize robust stability conditions next we consider the stabilization problem with memoryless and delayed feedback controllers based on our generalized robust stability conditions we obtain delay dependent sufficient conditions for the closed loop system to be robustly stable and give a design method of robustly stabilizing controllers finally we give two examples that illustrate our results we compare our conditions with other stability conditions and show our conditions are rather generalized

on maximum likelihood localization of coherent signals we consider the problem of localizing multiple signal sources in the special case where all the signals are known a priori to be coherent a maximum likelihood estimator mle is constructed for this special case and its asymptotical performance is analyzed via the cramer rao bound crb it is proved that the crb for this case is identical to the crb for the case that no prior knowledge on coherency is exploited thus establishing a quite surprising result that asymptotically the localization errors are not reduced by exploiting this prior knowledge also we prove that in the coherent case the deterministic signals model and the stochastic signals model yield mle s that are asymptotically identical simulation results confirming these theoretical results are included

reversible jump markov chain monte carlo computation and bayesian model determination markov chain monte carlo methods for bayesian computation have until recently been restricted to problems where the joint distribution of all variables has a density with respect to some fixed standard underlying measure they have therefore not been available for application to bayesian model determination where the dimensionality of the parameter vector is typically not fixed this paper proposes a new framework for the construction of reversible markov chain samplers that jump between parameter subspaces of differing dimensionality which is flexible and entirely constructive it should therefore have wide applicability in model determination problems the methodology is illustrated with applications to multiple change point analysis in one and two dimensions and to a bayesian comparison of binomial experiments

new delay dependent stabilization conditions of t s fuzzy systems with constant delay this paper focuses on the problem of robust control for takagi sugeno t s fuzzy systems with time delay the delay dependent stability analysis and controller synthesis have been addressed the free weighting matrix method has been used for stability analysis and controller synthesis new and less conservative delay dependent stability conditions are proposed in terms of linear matrix inequalities lmi finally some examples are given to illustrate the effectiveness of the proposed approaches

exponential and almost sure exponential stability of stochastic fuzzy delayed cohen grossberg neural networks in this paper we study a class of stochastic fuzzy delayed cohen grossberg neural networks two kinds of stability are discussed in our investigation one is exponential stability in the mean square and the other is almost sure exponential stability first some sufficient conditions are derived to guarantee the exponential stability in the mean square for the considered system based on the lyapunov krasovskii functional stochastic analysis theory and the ito s formula as well as the dynkin formula then we further investigate the almost sure exponential stability by employing the nonnegative semi martingale convergence theorem moreover we prove that the addressed system is both almost sure exponentially stable and exponentially stable in the mean square under suitable conditions finally three numerical examples are also given to show the effectiveness of the theoretical results in particular the simulation figures establish that fuzzy systems do have more advantages than non fuzzy systems

multivariate bayesian variable selection and prediction the multivariate regression model is considered with p regressors a latent vector with p binary entries serves to identify one of two types of regression coefficients those close to 0 and those not specializing our general distributional setting to the linear model with gaussian errors and using natural conjugate prior distributions we derive the marginal posterior distribution of the binary latent vector fast algorithms aid its direct computation and in high dimensions these are supplemented by a markov chain monte carlo approach to sampling from the known posterior distribution problems with hundreds of regressor variables become quite feasible we give a simple method of assigning the hyperparameters of the prior distribution the posterior predictive distribution is derived and the approach illustrated on compositional analysis of data involving three sugars with 160 near infra red absorbances as regressors

maximum likelihood methods for direction of arrival estimation five methods of direction of arrival doa estimation which can be derived from the maximum likelihood ml principle are considered the ml method mlm results from the application of the ml principle to the statistics of the observed raw data the standard multiple signal classification music procedure called music 1 is obtained as a brute force approximation of the mlm an improved music procedure named music 2 is obtained by applying the ml principle to the statistics of certain linear combinations of the sample noise space eigenvectors a procedure which compromises between the good performance of the mlm and the computational simplicity of music is a method of direction estimation mode 1 which is derived as a large sample realization of the mlm a fifth method called mode 2 is obtained by using the ml principle on the statistics of certain linear combinations of the sample eigenvectors mode 2 is computationally less demanding than the mlm it is of the same complexity as mode 1 and statistically more efficient a numerical comparison of these five doa estimation methods is presented it confirms the analytic results on their theoretical performance levels

variable selection via gibbs sampling abstract a crucial problem in building a multiple regression model is the selection of predictors to include the main thrust of this article is to propose and develop a procedure that uses probabilistic considerations for selecting promising subsets this procedure entails embedding the regression setup in a hierarchical normal mixture model where latent variables are used to identify subset choices in this framework the promising subsets of predictors can be identified as those with higher posterior probability the computational burden is then alleviated by using the gibbs sampler to indirectly sample from this multinomial posterior distribution on the set of possible subset choices those subsets with higher probability the promising ones can then be identified by their more frequent appearance in the gibbs sample

existence and global exponential stability of equilibrium for discrete time fuzzy bam neural networks with variable delays and impulses in this paper we study a class of discrete time fuzzy bam neural networks with variable delays and impulses based on m matrix theory and analytic methods some sufficient conditions are established for the existence and global exponential stability of a unique equilibrium moreover the exponential convergence rate index is estimated a numerical example is given to show the effectiveness of the obtained results in particular the simulation figures establish that fuzzy systems do have more advantages than non fuzzy systems

linear prediction approach to direction estimation of cyclostationary signals in multipath environment we investigate the estimation of the directions of arrival doa of closely spaced narrowband cyclostationary signals in the presence of multipath propagation by exploiting the spatial and temporal properties of most communication signals we propose a new cyclic forward backward linear prediction fblp approach for coherent signals impinging on a uniform linear array ula in the proposed algorithm the evaluation of the cyclic array covariance matrix is avoided and the difficulty of choosing the optimal time lag parameter is alleviated as a result the proposed approach has two advantages 1 the computational load is relatively reduced and 2 the robustness of estimation is significantly improved the performance of the proposed approach is confirmed through numerical examples and it is shown that this approach is superior in resolving the closely spaced coherent signals with a small length of array data and at relatively low signal to noise ratio snr

boundedness for impulsive delay differential equations and applications to population growth models abstract in this paper criteria on uniform ultimate boundedness are derived for impulsive delay differential equations using lyapunov functions and razumikhin techniques it should be noted that the boundedness criteria establish global existence of solutions as well as boundedness without assuming a priori that solutions can necessarily be continued to infinity those criteria are then used to obtain permanence results for population growth models some examples are discussed to illustrate the theorems

performance analysis of the cyclic music method of direction estimation for cyclostationary signals the cyclic music algorithm exploits the cyclostationarity exhibited by most man made communication signals to perform signal selective estimation of directions of arrival at an array of sensors in the paper the bias and mean squared error of these direction estimates are derived under the assumption that the signals are complex possibly non gaussian and cyclostationary the analytical results are shown to be excellent predictors of empirical results obtained from computer simulation of the cyclic music algorithm and are also compared with the cramer rao bound which in certain cases can be orders of magnitude less than the cramer rao bound for the directions of arrival of stationary signals

smoothed eigenspace based parameter estimation abstract contemporary high resolution direction of arrival doa techniques in sensor array processing based on the music algorithm fail when the signals are fully correlated and or closely spaced when the data are taken from a linear array of equally spaced sensors spatial smoothing or subarray averaging is the most familiar way to combat the effects of signal correlation and retain some computational efficiency the purpose of this paper is to show analytically that improved parameter estimates result when subarray averaging is applied to a suitable reduced rank approximation to the array covariance matrix instead of performing a conventional spatial smoothing further improvements can be achieved if a suitable data matrix is utilized simulation examples are provided to substantiate the theoretical predictions

delay fractioning approach to robust exponential stability of fuzzy cohen grossberg neural networks abstract in this paper the problem of robust exponential stability analysis for a class of takagi sugeno ts fuzzy cohen grossberg neural networks with uncertainties and time varying delays is investigated a generalized activation function is used and the assumptions such as boundedness monotony and differentiability of the activation functions are removed by using a lyapunov krasovskii functional and employing the delay fractioning approach a set of sufficient conditions are established for achieving the required result the obtained conditions are proposed in terms of linear matrix inequalities lmis so its feasibility can be checked easily via standard numerical toolboxs the main advantage of the proposed criteria lies in its reduced conservatism which is mainly based on the time delay fractioning technique in addition to that a numerical example with simulation results is given to show the effectiveness of the obtained lmi conditions

improved spatial smoothing techniques for doa estimation of coherent signals in the context of coherent signal classification spatial smoothing is necessary for the application of the eigen based direction of arrival doa estimation methods however the currently known spatial smoothing algorithms not only reduce the effective aperture of the array but also do not consider the cross correlations of the subarray outputs an improved spatial smoothing algorithm which can fully utilize the correlations of the array outputs and produce a more stable estimate of the covariance matrix is presented simulation results are provided to verify the theoretical prediction the superiority of this method over the conventional methods is obvious especially when the sosr subarray to overall size ratio is small