h_ infty filtering for nonlinear discrete time systems subject to quantization and packet dropouts this paper investigates the problem of h filtering for a class of nonlinear discrete time systems with measurement quantization and packet dropouts each output is transmitted via an independent communication channel and the phenomenon of packet dropouts in transmission is governed by an individual random binary distribution while the quantization errors are treated as sector bound uncertainties based on a piecewise lyapunov function an approach to the design of h piecewise filter is pro posed such that the filtering error system is stochastically stable with a guaranteed h performance some slack matrices are introduced to facilitate the filter design procedure by eliminating the coupling between the lyapunov matrices and the system matrices the filter parameters can be obtained by solving a set of linear matrix inequalities lmis which are numerically tractable with commercially available software finally two illustrative examples are provided to show the effectiveness of the proposed method

boundary value problems for nth order lipschitz equations on considere l unicite des solutions de problemes aux valeurs limites pour une equation scalaire d ordre n y n f t y y y n 1 ou f est continue sur a b r n et satisfait une condition de lipschitz

characterization of large price variations in financial markets statistics of drawdowns loss from the last local maximum to the next local minimum plays an important role in risk assessment of investment strategies as they incorporate higher two order correlations they offer a better measure of real market risks than the variance or other cumulants of daily or some other fixed time scale of returns previous results have shown that the vast majority of drawdowns occurring on the major financial markets have a distribution which is well represented by a stretched exponential while the largest drawdowns are occurring with a significantly larger rate than predicted by the bulk of the distribution and should thus be characterized as outliers eur phys j b 1 1998 141 j risk 2001 in the present analysis the definition of drawdowns is generalized to coarse grained drawdowns or so called e drawdowns and a link between such e outliers and preceding log periodic power law bubbles previously identified quantitative finance 1 2001 452 is established

network based robust h_infty filtering for uncertain linear systems the problem of network based robust hinfin filtering for uncertain linear systems is investigated different from the design of the traditional filter the effects of the network induced delay and data dropout on the performance of a filtering error system are considered the derived criteria for hinfin performance analysis of the filtering error system and filter design are expressed as a set of linear matrix inequalities which can be solved by using convex optimization method numerical examples show the effectiveness of the design method

novel stability criteria for t s fuzzy systems in this paper novel stability conditions for takagi sugeno t s fuzzy systems are presented the so called nonquadratic membership dependent lyapunov function is first proposed which is formulated in a higher order form of both the system states and the normalized membership functions than existing techniques in the literature then new membership dependent stability conditions are developed by the new lyapunov function approach it is shown that the conservativeness of the obtained criteria can be further reduced as the degree of the lyapunov function increases two numerical examples are given to demonstrate the effectiveness and less conservativeness of the obtained theoretical results

multifractal detrended cross correlation analysis for two nonstationary signals we propose a method called multifractal detrended cross correlation analysis to investigate the multifractal behaviors in the power law cross correlations between two time series or higher dimensional quantities recorded simultaneously which can be applied to diverse complex systems such as turbulence finance ecology physiology geophysics and so on the method is validated with cross correlated one and two dimensional binomial measures and multifractal random walks as an example we illustrate the method by analyzing two financial time series

detrending moving average algorithm for multifractals the detrending moving average dma algorithm is a widely used technique to quantify the long term correlations of nonstationary time series and the long range correlations of fractal surfaces which contains a parameter ensuremath theta determining the position of the detrending window we develop multifractal detrending moving average mfdma algorithms for the analysis of one dimensional multifractal measures and higher dimensional multifractals which is a generalization of the dma method the performance of the one dimensional and two dimensional mfdma methods is investigated using synthetic multifractal measures with analytical solutions for backward ensuremath theta 0 centered ensuremath theta 0 5 and forward ensuremath theta 1 detrending windows we find that the estimated multifractal scaling exponent ensuremath tau q and the singularity spectrum f ensuremath alpha are in good agreement with the theoretical values in addition the backward mfdma method has the best performance which provides the most accurate estimates of the scaling exponents with lowest error bars while the centered mfdma method has the worse performance it is found that the backward mfdma algorithm also outperforms the multifractal detrended fluctuation analysis the one dimensional backward mfdma method is applied to analyzing the time series of shanghai stock exchange composite index and its multifractal nature is confirmed

optimality for boundary value problems for lipschitz equations on considere les solutions de problemes aux valeurs limites pour une equation scalaire d ordre n x n f t x x x n 1 ou f est continue sur a b r n et satisfait une condition de lipschitz on determine les sous intervalles de longueur optimale de a b en fonctions des coefficients de lipschitz sur lesquels des solutions sont uniques

fuzzy model based piecewise mathscr h _ infty static output feedback controller design for networked nonlinear systems this paper investigates the problem of robust h output feedback control for a class of nonlinear systems under unreliable communication links the nonlinear plant is represented by a takagi sugeno t s uncertain fuzzy model and the communication links between the plant and controller are assumed to be imperfect i e data packet dropouts occur intermittently which is often the case in a network environment stochastic variables that satisfy the bernoulli random binary distribution are adopted to characterize the data missing phenomenon and the attention is focused on the design of a piecewise static output feedback sof controller such that the closed loop system is stochastically stable with a guaranteed h performance based on a piecewise lyapunov function combined with some novel convexifying techniques the solutions to the problem are formulated in the form of linear matrix inequalities lmis finally simulation examples are also provided to illustrate the effectiveness of the proposed approaches

robust h sub spl infin control for uncertain discrete time delay fuzzy systems via output feedback controllers this work investigates the problem of robust output feedback h sub spl infin control for a class of uncertain discrete time fuzzy systems with time delays the state space takagi sugeno fuzzy model with time delays and norm bounded parameter uncertainties is adopted the purpose is the design of a full order fuzzy dynamic output feedback controller which ensures the robust asymptotic stability of the closed loop system and guarantees an h sub spl infin norm bound constraint on disturbance attenuation for all admissible uncertainties in terms of linear matrix inequalities lmis a sufficient condition for the solvability of this problem is presented explicit expressions of a desired output feedback controller are proposed when the given lmis are feasible the effectiveness and the applicability of the proposed design approach are demonstrated by applying this to the problem of robust h sub spl infin control for a class of uncertain nonlinear discrete delay systems

study of cross correlation in a self affine time series of taxi accidents abstract we study in this paper the cross correlation between self affine time series of real variables recorded simultaneously in cases of taxi accidents for this purpose we apply the dcca method and show that the cross correlation can be divided into three distinct groups if we look for the detrended covariance function i e long range cross correlations short range cross correlations and no cross correlations finally it will be seen that the detrended covariance function is robust if compared with other methods in identifying these types of cross correlations

random matrix approach to cross correlations in financial data we analyze cross correlations between price fluctuations of different stocks using methods of random matrix theory rmt using two large databases we calculate cross correlation matrices c of returns constructed from i 30 min returns of 1000 us stocks for the 2 yr period 1994 1995 ii 30 min returns of 881 us stocks for the 2 yr period 1996 1997 and iii 1 day returns of 422 us stocks for the 35 yr period 1962 1996 we test the statistics of the eigenvalues lambda i of c against a null hypothesis a random correlation matrix constructed from mutually uncorrelated time series we find that a majority of the eigenvalues of c fall within the rmt bounds lambda lambda for the eigenvalues of random correlation matrices we test the eigenvalues of c within the rmt bound for universal properties of random matrices and find good agreement with the results for the gaussian orthogonal ensemble of random matrices implying a large degree of randomness in the measured cross correlation coefficients further we find that the distribution of eigenvector components for the eigenvectors corresponding to the eigenvalues outside the rmt bound display systematic deviations from the rmt prediction in addition we find that these deviating eigenvectors are stable in time we analyze the components of the deviating eigenvectors and find that the largest eigenvalue corresponds to an influence common to all stocks our analysis of the remaining deviating eigenvectors shows distinct groups whose identities correspond to conventionally identified business sectors finally we discuss applications to the construction of portfolios of stocks that have a stable ratio of risk to return less

output feedback control for t s fuzzy delta operator systems with time varying delays via an input output approach in this paper the problem of induced l 2 disturbance attenuation control is investigated for discrete time takagi sugeno t s fuzzy delta operator systems with time varying delays and disturbance input via an input output approach a model transformation method is utilized to approximate the time varying delay in t s fuzzy delta operator systems by applying the scaled small gain ssg theorem and lyapunov krasovskii functional approach a sufficient condition is established to guarantee that the closed loop system is asymptotically stable and has an induced l 2 disturbance attenuation performance the existence condition of the dynamic output feedback controller can be solved via convex optimization problems finally simulation results are given to demonstrate the feasibility and effectiveness of the proposed method

fuzzy affine model based memory filter design of nonlinear systems with time varying delay this paper studies the piecewise affine memory mathscr h infty filtering problem for nonlinear systems with time varying delay in a delay dependent framework the nonlinear plant is characterized by a continuous time takagi sugeno fuzzy affine model with parametric uncertainties the purpose is to develop a new approach for filter synthesis procedure with less conservatism specifically by constructing a novel lyapunov krasovskii functional together with a wirtinger based integral inequality reciprocally convex inequality and s procedure an improved criterion is first attained for analyzing the mathscr h infty performance of the filtering error system and then via some linearization techniques the piecewise affine memory filter synthesis is carried out it is shown that the existence of desired filter gains can be explicitly determined by the solution of a convex optimization problem finally simulation studies are presented to reveal the effectiveness and less conservatism of the developed approaches it is anticipated that the proposed scheme can be further extended to the analysis and synthesis of continuous time fuzzy affine dynamic systems with integrated communication delays in the networked circumstance

h_ infty filter design for a class of networked control systems via t s fuzzy model approach this paper is concerned with h design for a class of networked control systems ncss with multiple state delays via the takagi sugeno t s fuzzy model the transfer delays and packet loss that are induced by the limited bandwidth of communication networks are considered the focus of this paper is on the analysis and design of a full order h filter such that the filtering error dynamics are stochastically stable and a prescribed h attenuation level is guaranteed sufficient conditions are established for the existence of the desired filter in terms of linear matrix inequalities lmis an example is given to illustrate the effectiveness and applicability of the proposed design method

robust filter for nonlinear stochastic partial differential systems in sensor signal processing fuzzy approach this study addresses a robust h filter design problem for nonlinear stochastic partial differential systems nspdss with random external disturbance and measurement noise in the spatiotemporal domain for nspdss a robust h filter design through a set of sensor measurements needs to solve a complex second order hamilton jacobi integral inequality hjii in order to simplify the design procedure a fuzzy stochastic partial differential system based on a fuzzy interpolation approach is proposed to approximate the nspds then a fuzzy stochastic spatial state space model is developed to represent the fuzzy stochastic partial differential system via the semidiscretization finite difference scheme and the kronecker product based on this model a robust h filter design is proposed to achieve the robust state estimation via solving linear matrix inequalities lmis instead of a second order hjii the proposed robust fuzzy h filter efficiently attenuates the effect of spatiotemporal external disturbances and measurement noise on the state estimation of nspdss from the area energy point of view finally a robust h state estimation example of heat transfer system is given to illustrate the design procedure and to confirm the h filtering performance of the proposed robust filter design method

network based h control for t s fuzzy systems with an adaptive event triggered communication scheme abstract this paper extends the prior work of discrete event triggered communication for networked t s fuzzy systems where the next communication instant is determined by an event triggered communication scheme with a constant threshold and the premises of t s fuzzy systems and parallel distribution compensation pdc fuzzy control rules are synchronous firstly an adaptive event triggered communication scheme for ncss is presented which relaxes the assumption of the prior work that the threshold of event triggered communication scheme is a preselected constant secondly an asynchronous premise re construct method for networked t s fuzzy systems is proposed which relaxes the assumption of the prior work that the premises of the plant and the pdc control rules are synchronous we show that the number of transmitted packets generated by the proposed scheme is smaller than those generated by some existing ones while ensuring the desired control performance simulations show the effectiveness of the proposed method

filtering for nonlinear genetic regulatory networks with stochastic disturbances in this paper the filtering problem is investigated for nonlinear genetic regulatory networks with stochastic disturbances and time delays where the nonlinear function describing the feedback regulation is assumed to satisfy the sector condition the stochastic perturbation is in the form of a scalar brownian motion and the time delays exist in both the translation process and the feedback regulation process the purpose of the addressed filtering problem is to estimate the true concentrations of the mrna and protein specifically we are interested in designing a linear filter such that in the presence of time delays stochastic disturbances as well as sector nonlinearities the filtering dynamics of state estimation for the stochastic genetic regulatory network is exponentially mean square stable with a prescribed decay rate lower bound beta by using the linear matrix inequality lmi technique sufficient conditions are first derived for ensuring the desired filtering performance for the gene regulatory model and the filter gain is then characterized in terms of the solution to an lmi which can be easily solved by using standard software packages a simulation example is exploited in order to illustrate the effectiveness of the proposed design procedures

robust finite time estimation of markovian jumping systems with bounded transition probabilities this paper studies the finite time state estimation and h filter design problem for a class of uncertain markovian jumping systems mjss with bounded transition probabilities based on the selected lyapunov krasovskii functional the finite time h filter is constructed to derive a sufficient condition such that the filtering error dynamic mjss are finite time bounded and satisfies a prescribed level of h disturbance attenuation in a finite time interval then in terms of linear matrix inequalities lmis techniques the sufficient condition on the existence of finite time h filter is presented and proved the filter matrices can be solved directly by using the existing lmis optimization techniques finally two numerical examples are given to illustrate the effectiveness of the proposed design approach

universal and nonuniversal properties of cross correlations in financial time series we use methods of random matrix theory to analyze the cross correlation matrix c of stock price changes of the largest 1000 u s companies for the 2 year period 1994 1995 we find that the statistics of most of the eigenvalues in the spectrum of c agree with the predictions of random matrix theory but there are deviations for a few of the largest eigenvalues we find that c has the universal properties of the gaussian orthogonal ensemble of random matrices furthermore we analyze the eigenvectors of c through their inverse participation ratio and find eigenvectors with large ratios at both edges of the eigenvalue spectrum a situation reminiscent of localization theory results

sampled data fuzzy stabilization of nonlinear systems under nonuniform sampling this paper investigates the sampled data fuzzy stabilization problem for a class of nonlinear systems that is exactly modeled in takagi sugeno fuzzy form at least locally a new method for designing parallel distribution compensation fuzzy controller is proposed which just requires that the nonlinear function is locally lipschitz by considering the sample and hold behavior of the system and using jensen s integral inequality an inequality constrain condition is derived from the locally lipschitz property further by defining a time dependent lyapunov krasovskii functional term a new technique instead of the use of s procedure is developed and stabilization conditions for state feedback and observer based output feedback under nonuniform sampling are obtained compared with the existing ones the new design method not only avoids the difficulty of finding exact upper bounds of asynchronous errors of mismatch membership functions but contains less conservatism and less numerical complexity as well finally some illustrative examples are given to show the effectiveness of the proposed design method and the significant improvement over the existing results

nonfragile h_ infty fuzzy filtering with randomly occurring gain variations and channel fadings this paper is concerned with the nonfragile h infty filtering problem for a class of discrete time takagi sugeno t s fuzzy systems with both randomly occurring gain variations rogvs and channel fadings the phenomenon of the rogvs is introduced into the system model so as to account for the parameter fluctuations occurring during the filter implementation two sequences of random variables obeying the bernoulli distribution are employed to describe the phenomenon of the rogvs bounded by prescribed norms in addition the rice fading model is utilized to describe the phenomena of channel fadings where the occurrence probabilities of the random channel coefficients are allowed to time varying through stochastic analysis and lyapunov functional approach sufficient conditions are established under which the filtering error dynamics is exponentially mean square stable with a prespecified h infty performance the set of the desired nonfragile h infty filters is characterized by solving a convex optimization problem via the semidefinite programming method an illustrative example is given to show the usefulness and effectiveness of the proposed design method in this paper

dynamic output feedback dissipative control for t s fuzzy systems with time varying input delay and output constraints this paper develops a new fuzzy dynamic output feedback control scheme for takagi sugeno t s fuzzy systems with time varying input delay and output constraints based on q s r alpha dissipativity the proposed controller called a q s r alpha dissipative output feedback fuzzy controller takes into consideration the abstract energy storage function and supply rate for the disturbance attenuation and provides a unified framework that can incorporate existing results for mathcal h infty and passivity controllers as special cases for t s fuzzy systems with time varying input delay and output constraints a dynamic parallel distributed compensator is used to design the q s r alpha dissipative output feedback fuzzy controller to ensure the asymptotic stability and strict q s r alpha dissipativity of closed loop systems described by a t s fuzzy model that satisfies some output constraints by employing the reciprocally convex approach a new set of delay dependent conditions for the desired controller is formulated in terms of the linear matrix inequality the effectiveness and the applicability of the proposed design techniques are validated by an example of control for active suspension systems for different road conditions

diffusion entropy analysis on the scaling behavior of financial markets abstract in this paper the diffusion entropy technique is applied to investigate the scaling behavior of financial markets the scaling behaviors of four representative stock markets dow jones industrial average standardp with the scale invariance exponents all in the interval 0 92 0 95 we also estimate the local scaling exponents which indicate the financial time series is homogenous perfectly in addition a parsimonious percolation model for stock markets is proposed of which the scaling behavior agrees with the real life markets well

kalman filtering with intermittent observations motivated by navigation and tracking applications within sensor networks we consider the problem of performing kalman filtering with intermittent observations when data travel along unreliable communication channels in a large wireless multihop sensor network the effect of communication delays and loss of information in the control loop cannot be neglected we address this problem starting from the discrete kalman filtering formulation and modeling the arrival of the observation as a random process we study the statistical convergence properties of the estimation error covariance showing the existence of a critical value for the arrival rate of the observations beyond which a transition to an unbounded state error covariance occurs we also give upper and lower bounds on this expected state error covariance

fuzzy approximation based adaptive control of nonlinear delayed systems with unknown dead zone this paper focuses on the problem of adaptive fuzzy control for a class of nonlinear systems without a strict feedback form a new adaptive fuzzy control scheme is proposed for nonlinear delayed systems with unknown dead zone nonlinearity control design is achieved by introducing a variable separation approach and employing the unique structural property of fuzzy logic systems the proposed adaptive fuzzy controller guarantees that all the closed loop signals are semiglobally uniformly ultimately bounded and the tracking error converges to a small neighborhood of the origin two simulation examples illustrate the effectiveness of the proposed approach

log periodic self similarity an emerging financial law abstract a hypothesis that the financial log periodicity cascading self similarly through various time scales carries signatures of a law is pursued it is shown that the most significant historical financial events can be classified amazingly well using a single and unique value of the preferred scaling factor 2 which indicates that its real value should be close to this number this applies even to a declining decelerating log periodic phase crucial in this connection is identification of a super bubble bubble on bubble phenomenon identifying a potential universal preferred scaling factor as undertaken here may significantly improve the predictive power of the corresponding methodology several more specific related results include evidence that i the real end of the high technology bubble on the stock market started with a decelerating log periodic draw down in the beginning of september 2000 ii a parallel 2000 2002 decline seen in the standard poor s 500 from the log periodic perspective is already of the same significance as the one of the early 1930s and of the late 1970s iii all this points to a much more serious global crash in around 2025 of course from a level much higher at least one order of magnitude than in 2000

robust stability of uncertain stochastic differential delay equations in this paper we first discuss the robust stability of uncertain linear stochastic differential delay equations we then extend the theory to cope with the robust stability of uncertain semi linear stochastic differential delay equations we shall also give several examples to illustrate our theory

on energy to peak filtering for nonuniformly sampled nonlinear systems a markovian jump system approach this paper focuses on the filter design for nonuniformly sampled nonlinear systems which can be approximated by takagi sugeno t s fuzzy systems the sampling periods of the measurements are time varying and the nonuniform observations of the outputs are modeled by a homogenous markov chain a mode dependent estimator with a fast sampling frequency is proposed such that the estimation can track the signal to be estimated with the nonuniformly sampled outputs the nonlinear systems are discretized with the fast sampling period by using an augmentation technique the corresponding stochastic estimation error system is obtained by studying the stochastic stability and the energy to peak performance of the estimation error system we derive the linear matrix inequality based sufficient conditions the parameters of the mode dependent estimator can be calculated by using the proposed iterative algorithm two examples are used to demonstrate the design procedure and the efficacy of the proposed design method

relaxed stabilization criterion for t s fuzzy systems by minimum type piecewise lyapunov function based switching fuzzy controller this paper proposes the minimum type piecewise lyapunov function based switching fuzzy controller that switches accompanying the piecewise lyapunov function by applying this switching fuzzy controller with the minimum type piecewise lyapunov function the relaxed stabilization criterion is obtained for continuous takagi sugeno t s fuzzy systems some conditions of the relaxed stabilization criterion are represented by bilinear matrix inequalities bmis which contain some bilinear terms as the product of a full matrix and a scalar according to the literature the path following method is very effective for this kind of bmi problem hence it is utilized to obtain solutions of the criterion xie et al in 1997 chose two types i e minimum type and maximum type of piecewise lyapunov functions as the lyapunov function candidates the reasons for why this study only chooses the minimum type piecewise lyapunov function as the lyapunov function candidate are illustrated moreover the numerical example shows the relaxation of the proposed criterion

multifractal detrending moving average cross correlation analysis there are a number of situations in which several signals are simultaneously recorded in complex systems which exhibit long term power law cross correlations the multifractal detrended cross correlation analysis mfdcca approaches can be used to quantify such cross correlations such as the mfdcca based on the detrended fluctuation analysis mfxdfa method we develop in this work a class of mfdcca algorithms based on the detrending moving average analysis called mfxdma the performances of the proposed mfxdma algorithms are compared with the mfxdfa method by extensive numerical experiments on pairs of time series generated from bivariate fractional brownian motions two component autoregressive fractionally integrated moving average processes and binomial measures which have theoretical expressions of the multifractal nature in all cases the scaling exponents h xy extracted from the mfxdma and mfxdfa algorithms are very close to the theoretical values for bivariate fractional brownian motions the scaling exponent of the cross correlation is independent of the cross correlation coefficient between two time series and the mfxdfa and centered mfxdma algorithms have comparative performances which outperform the forward and backward mfxdma algorithms for two component autoregressive fractionally integrated moving average processes we also find that the mfxdfa and centered mfxdma algorithms have comparative performances while the forward and backward mfxdma algorithms perform slightly worse for binomial measures the forward mfxdma algorithm exhibits the best performance the centered mfxdma algorithms performs worst and the backward mfxdma algorithm outperforms the mfxdfa algorithm when the moment order q 0 we apply these algorithms to the return time series of two stock market indexes and to their volatilities for the returns the centered mfxdma algorithm gives the best estimates of h xy q since its h xy 2 is closest to 0 5 as expected and the mfxdfa algorithm has the second best performance for the volatilities the forward and backward mfxdma algorithms give similar results while the centered mfxdma and the mfxdfa algorithms fail to extract rational multifractal nature

a fault detection approach for nonlinear systems based on data driven realizations of fuzzy kernel representations this paper is devoted to the data driven fault detection of nonlinear systems for our purpose the definition of takagi sugeno fuzzy data driven forms of kernel representations for nonlinear systems is introduced first which builds the basis of our work the major contributions consist of two parts in the first part a data driven method for fuzzy process modeling is proposed and associated with it some modeling issues are addressed with the aid of the so called randomized algorithm technique in the probabilistic framework it is followed by a data driven realization of fuzzy kernel representation and its implementation in the fault detection system design to link the data driven methods to the well established observer based fault detection approaches the recursive form of the fuzzy kernel representation is proposed in the second part the fuzzy observer based fault detection design scheme is investigated based on the recursive fuzzy kernel representation the main results of our study are illustrated by an experimental study on the laboratory setup of a three tank system

discrete scale invariance in cross correlations between time series abstract the de trended cross correlation analysis dcca is converted to a new form which turns out to be a periodic function modulated power law to evaluate discrete scale long range cross correlation between time series if the modulator is dominated with one frequency the derived form will degenerate to a log periodic power law we investigate a total of five important stock markets distributing in different continents calculations show that the cross correlations between different stock markets may hint at log periodic oscillations this finding may be helpful for us to evaluate financial state in a global way

a novel dropout compensation scheme for control of networked t s fuzzy dynamic systems this paper considers the problem of h state feedback control for networked nonlinear systems under unreliable communication links with packet dropouts the nonlinear plant in this paper is described by a takagi sugeno t s fuzzy model the packet dropouts in both sensor to controller s c and controller to actuator c a channels are considered which are modeled by bernoulli processes a new compensation scheme for the estimation of missing packets is proposed and a piecewise state feedback controller is designed so that the resulting closed loop control system is stochastically stable with guaranteed h performance then the results are extended to the case when both network induced delays and packet dropouts exist in communication links finally some numerical examples are given to illustrate the procedure of the proposed approaches and the optimal h performance in comparison to the existing approaches

nonsynchronized state estimation of multichannel networked nonlinear systems with multiple packet dropouts via t s fuzzy affine dynamic models this paper investigates the problem of robust h state estimation for a class of multichannel networked nonlinear systems with multiple packet dropouts the nonlinear plant is represented by takagi sugeno t s fuzzy affine dynamic models with norm bounded uncertainties and stochastic variables with general probability distributions are adopted to characterize the data missing phenomenon in output channels the objective is to design an admissible state estimator guaranteeing the stochastic stability of the resulting estimation error system with a prescribed h disturbance attenuation level it is assumed that the plant premise variables which are often the state variables or their functions are not measurable so that the estimator implementation with state space partition may not be synchronized with the state trajectories of the plant based on a piecewise quadratic lyapunov function combined with s procedure and some matrix inequality convexifying techniques two different approaches are developed to robust filtering design for the underlying t s fuzzy affine systems with unreliable communication links all the solutions to the problem are formulated in the form of linear matrix inequalities lmis finally simulation examples are provided to illustrate the effectiveness of the proposed approaches

cross correlations between volume change and price change in finance one usually deals not with prices but with growth rates r defined as the difference in logarithm between two consecutive prices here we consider not the trading volume but rather the volume growth rate r the difference in logarithm between two consecutive values of trading volume to this end we use several methods to analyze the properties of volume changes r and their relationship to price changes r we analyze 14 981 daily recordings of the standard and poor s s p 500 index over the 59 year period 1950 2009 and find power law cross correlations between r and r by using detrended cross correlation analysis dcca we introduce a joint stochastic process that models these cross correlations motivated by the relationship between r and r we estimate the tail exponent of the probability density function p r r 1 for both the s p 500 index as well as the collection of 1819 constituents of the new york stock exchange composite index on 17 july 2009 as a new method to estimate we calculate the time intervals q between events where r q we demonstrate that q the average of q obeys q q we find 3 furthermore by aggregating all q values of 28 global financial indices we also observe an approximate inverse cubic law

h state feedback controller design for continuous time t s fuzzy systems in finite frequency domain this paper is concerned with finite frequency h state feedback controller designs for continuous time takagi sugeno t s fuzzy systems a parallel distributed compensation pdc controller is designed in finite frequency domain with the aid of the generalized kalman yakubovich popov gkyp lemma the design problem is formulated as solving a set of linear matrix inequalities lmis simulation examples illustrate that the finite frequency design approach proposed in this paper receives better results than those of existing full frequency approaches

delay dependent hbox h _ infty filter design for discrete time fuzzy systems with time varying delays this paper investigates delay dependent hinfin filter design problems for discrete time fuzzy systems with time varying delays first a novel delay dependent piecewise lyapunov krasovskii functional ddplkf is proposed in which both the upper bound of delays and the delay interval are considered based on this ddplkf the delay dependent stability criteria for discrete time systems with constant or time varying delays are obtained respectively then delay dependent full order and reduced order hinfin filter design approaches are proposed the filter parameters can be obtained by solving a set of linear matrix inequalities lmis simulation examples are also given to illustrate the performance of the proposed approaches it is shown that our approaches are less conservative and that the corresponding hinfin filters can achieve better performance than the existing approaches

decentralized fuzzy control of nonlinear interconnected dynamic delay systems via mixed h_2 h_ infty optimization with smith predictor each subsystem of a nonlinear interconnected dynamic delayed system is approximated by a weighted combination of l transfer function delayed systems tfdss the h2 norm of the difference between the transfer function of a reference model and the closed loop transfer function of the kth tfds of subsystem i is then minimized to obtain a suitable frequency response without incurring oscillating and sluggish phenomena because of the existence of the disturbance at the output of the kth tfds which is not only large but also contains various frequency components the h norm of the weighted sensitivity function between the output disturbance and its corresponding output of the kth tfds is simultaneously minimized to reduce its effect furthermore with proper selection of weighted sensitivity functions certain specific modes of the output disturbance can be eliminated finally two simulations are performed one is the simulation of our designed tfdss with different delays or nonminimum phases and the other is the simulation of an internet based intelligent space for the trajectory tracking of a car like wheeled robot system we demonstrate the effectiveness and efficiency of the proposed control the main contributions of this paper are twofold first the control can simultaneously attain robust performance through the mixed h2 h optimization with a smith predictor and achieve the robust stability via l2n stable with finite gain second fuzzy observer is not needed

feedback fuzzy control for quantized networked systems with random delays this paper investigates the problem of feedback fuzzy control design for networked systems involving both random measurement and actuation delays and subject to quantization and random packet dropout it is assumed that system state or output signal is quantized before being communicated the paper focuses initially on an observer based output feedback fuzzy controller design and then derives a state feedback controller as special case sufficient conditions for the existence of an admissible fuzzy controller are established to ensure the exponential stability of the resulting closed loop fuzzy system simulation experiments on a lab scale four tank system to demonstrate the applicability of the designed fuzzy control algorithm

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

fault detection and isolation for affine fuzzy systems with sensor faults this paper investigates the fault detection and isolation fdi problem for a class of nonlinear systems with sensor outage faults the considered nonlinear systems are described as affine fuzzy models and the system outputs are chosen as the premise variables of fuzzy models different from the existing results the influence of sensor faults on premise variables is considered as a result the well known parallel distributed compensation scheme cannot be used for fdi filters design by using the structural information encoded in the fuzzy rules the affine fuzzy system is represented by multiple operating regime based models in fault free case and faulty cases in the multiple model scheme a bank of piecewise fdi filters are constructed each of them is based on the affine fuzzy model that describes the system in the presence of a specified fault the fault dependent residual signals generated from the filters are used for detecting and isolating the specified fault the fdi filter design conditions are obtained in the formulation of linear matrix inequalities finally a numerical example is given to illustrate the effectiveness and merits of the proposed method

uniqueness existence and optimality for fourth order lipschitz equations for each t y y y y4 t zi z2 z3 z e a 6 x r4 in particular we will characterize optimal length subintervals of a b in terms of the lipschitz coefficients k i 1 2 3 4 on which certain two three and four point boundary value problems for 1 have unique solutions the techniques we employ here involve applications of the pontryagin maximum principle 14 p 3141 in conjunction with uniqueness implies existence results for solutions of boundary value problems for 1 these techniques are motivated by works of melentsova 15 and melentsova and mil shtein 16 171 and most notably by the two papers by jackson ll 121 furthermore the results contained herein can be considered as extensions of a recent paper by henderson 6 dealing with boundary value problems for third order equations in relating the results of this paper to previous works we will formulate the boundary value problems in terms of the nth order differential equation

long range correlation and predictability of chinese stock prices abstract it is well known that the price time series of an efficient market display a brownian motion in such a case the best prediction of a future price is the last known price thus no arbitrage is possible a real market is however possibly not perfectly efficient in this paper we apply an error back propagation neural network to tick by tick high frequency time series of chinese a share stock prices and try to predict their future values the performance of the predictions varies largely for different stocks in order to seek for the dependence of the performance we study the long range correlation lrc of the data by the conventional detrended fluctuation analysis dfa and its generalization the multifractal detrended fluctuation analysis mfdfa clear relevance between the performance and the lrc is observed the prediction is particularly good when the time series are strongly anti persistent

non fragile h filter design for discrete time fuzzy systems with multiplicative gain variations this paper investigates the problem of non fragile h filter design for discrete time takagi sugeno t s fuzzy systems the filter to be designed is assumed to have two type of multiplicative gain variations by introducing additional matrix variables a relaxed h filtering analysis criterion is presented for the fuzzy system based on the fuzzy lyapunov function a filter state transformation approach is adopted to design the non fragile h filter and the filter gains are given in terms of the feasible solutions of a set of linear matrix inequalities lmis a simulation example is given to show the efficiency of the proposed methods

robust fault detection with missing measurements this paper investigates the problem of robust fault detection for uncertain systems with missing measurements the parameter uncertainty is assumed to be of polytopic type and the measurement missing phenomenon which appears typically in a network environment is modelled by a stochastic variable satisfying the bernoulli random binary distribution the focus is on the design of a robust fault detection filter or a residual generation system which is stochastically stable and satisfies a prescribed disturbance attenuation level this problem is solved in the parameter dependent framework which is much less conservative than the quadratic approach both full order and reduced order designs are considered and formulated via linear matrix inequality lmi based convex optimization problems which can be efficiently solved via standard numerical software a continuous stirred tank reactor cstr system is utilized to illustrate the design procedures

fault detection in finite frequency domain for t s fuzzy systems with partly unmeasurable premise variables abstract this paper is concerned with the problem of finite frequency fault detection fd for a class of t s fuzzy systems with partly unmeasurable premise variables to fully use the available information of the considered t s fuzzy system an fd observer whose premise variables consist of the measurable premise variables and the estimations of unmeasurable ones of the fuzzy model is designed however the membership functions of the fuzzy model and the observer to be designed are unsynchronized such that the existing fd methods based on parallel distribute compensation pdc strategy may be infeasible to overcome this difficulty a novel non pdc h h unknown input observer is designed to achieve the purpose of fault detection and provide less conservative results finally two simulation examples are given to illustrate the validity and merits of the presented fd scheme

robust nonfragile h_ infty filtering of fuzzy systems with linear fractional parametric uncertainties this paper is concerned with the h filtering problem for continuous time takagi sugeno t s fuzzy systems different from existing results for fuzzy h filtering the proposed ones are toward uncertain fuzzy systems with linear fractional parametric uncertainties attention is focused on the design of a fuzzy filter such that the filtering error system preserves a prescribed h performance where the filter to be designed is assumed to have gain variations by a descriptor representation approach two sufficient conditions for the h filter design are proposed in terms of linear matrix inequalities lmis when these lmis are feasible an explicit expression of the desired filter is given a simulation example will be given to show the efficiency of the proposed methods

asynchronous filtering of nonlinear markov jump systems with randomly occurred quantization via t s fuzzy models this paper focuses on the dissipativity based asynchronous filtering problem for a class of discrete time takagi sugeno fuzzy markov jump systems subject to randomly occurred quantization considering the random fluctuations of network conditions the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way to take full advantage of the partial information of system modes for the desired system performance we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous the mode dependent time varying delays are introduced which have different bounds for different system modes via fuzzy mode dependent lyapunov functional approach that can reduce conservatism a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly mathcal q mathcal s mathcal r dissipative then the gains of the filter are obtained by solving a set of linear matrix inequalities lmis an example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices delays quantization parameter and the degree of asynchronous jumps are given

relaxed stability and stabilization conditions of networked fuzzy control systems subject to asynchronous grades of membership this paper presents relaxed stability and stabilization conditions for takagi sugeno t s fuzzy systems under network environments subject to asynchronous grades of membership because of the introduction of a communication network the favorable property in the point to point connection that is sharing the identical premises in the fuzzy plant and the fuzzy controllers cannot be arbitrarily employed to widen the applicability of the fuzzy control method under network environments a novel method is provided to reconstruct the synchronous time scale grades of membership at the controller as those in the plant as a result the aforementioned favorable property can be conditionally used in the derivation of the stability and stabilization criteria for the system under consideration controlled over a communication network numerical examples have been given to illustrate the effectiveness of the proposed approach

weighted fuzzy observer based fault detection approach for discrete time nonlinear systems via piecewise fuzzy lyapunov functions the main focus of this paper is on the analysis and integrated design of mathcal l 2 observer based fault detection fd systems for discrete time nonlinear industrial processes to gain a deeper insight into this fd framework the existence condition is introduced first then an integrated design of mathcal l 2 observer based fd approach is realized by solving the proposed existence condition with the aid of takagi sugeno fuzzy dynamic modeling technique and piecewise fuzzy lyapunov functions most importantly a weighted piecewise fuzzy observer based residual generator is proposed aiming at achieving an optimal integration of residual evaluation and threshold computation into fd systems the core of this approach is to make use of the knowledge provided by fuzzy models of each local region and then to weight the local residual signal by means of different weighting factors in comparison with the standard norm based fuzzy observer based fd methods the proposed scheme may lead to a significant improvement of the fd performance in the end the effectiveness of the proposed method is verified by a numerical example and a case study on the laboratory setup of continuous stirred tank heater plant

co design of event generator and filtering for a class of t s fuzzy systems with stochastic sensor faults this paper is firstly concerned with reliable h filter design for a class of t s fuzzy systems with stochastic sensor faults under an event triggered scheme 1 a t s fuzzy model is used to approximate the nonlinear dynamics of the plant a set of stochastic variables are used to describe the sensor failure 2 an event triggered scheme which has some advantages over some existing ones is introduced to the networked control systems under the event triggered scheme the sensor data are transmitted only when the sampled measurements of the plant violate the specified event condition then an event based filter design model for t s fuzzy systems is constructed by taking the effect of event triggered scheme and the sensor faults into consideration sufficient conditions for the existence of the desired filter are established in terms of linear matrix inequalities and the explicit expression is given for the designed filter parameters a numerical example is provided to illustrate the design method

evolution of correlation structure of industrial indices of u s equity markets we investigate the dynamics of correlations present between pairs of industry indices of u s stocks traded in u s markets by studying correlation based networks and spectral properties of the correlation matrix the study is performed by using 49 industry index time series computed by k french and e fama during the time period from july 1969 to december 2011 which spans more than 40 years we show that the correlation between industry indices presents both a fast and a slow dynamics the slow dynamics has a time scale longer than 5 years showing that a different degree of diversification of the investment is possible in different periods of time moreover we also detect a fast dynamics associated with exogenous or endogenous events the fast time scale we use is a monthly time scale and the evaluation time period is a 3 month time period by investigating the correlation dynamics monthly we are able to detect two examples of fast variations in the first and second eigenvalue of the correlation matrix the first occurs during the dot com bubble from march 1999 to april 2001 and the second occurs during the period of highest impact of the subprime crisis from august 2008 to august 2009

reliable h_ infty control for discrete time fuzzy systems with infinite distributed delay in this paper the problem of reliable h control is investigated for discrete time takagi sugeno t s fuzzy systems with infinite distributed delay and actuator faults a discrete time homogeneous markov chain is used to represent the stochastic behavior of actuator faults in terms of a stochastic fuzzy lyapunov functional a sufficient condition is proposed to ensure that the resultant closed loop system is exponentially stable in the mean square sense with an h performance index based on the derived condition the reliable h control problem is solved and an explicit expression of the desired controller is also given the case of no failure in the actuator is also considered a numerical example is given to demonstrate that our results are effective and less conservative

variance constrained filtering for uncertain stochastic systems with missing measurements in this note we consider a new filtering problem for linear uncertain discrete time stochastic systems with missing measurements the parameter uncertainties are allowed to be norm bounded and enter into the state matrix the system measurements may be unavailable i e missing data at any sample time and the probability of the occurrence of missing data is assumed to be known the purpose of this problem is to design a linear filter such that for all admissible parameter uncertainties and all possible incomplete observations the error state of the filtering process is mean square bounded and the steady state variance of the estimation error of each state is not more than the individual prescribed upper bound it is shown that the addressed filtering problem can effectively be solved in terms of the solutions of a couple of algebraic riccati like inequalities or linear matrix inequalities the explicit expression of the desired robust filters is parameterized and an illustrative numerical example is provided to demonstrate the usefulness and flexibility of the proposed design approach

piecewise quadratic stability of fuzzy systems presents an approach to stability analysis of fuzzy systems the analysis is based on lyapunov functions that are continuous and piecewise quadratic the approach exploits the gain scheduling nature of fuzzy systems and results in stability conditions that can be verified via convex optimization over linear matrix inequalities examples demonstrate the many improvements over analysis based on a single quadratic lyapunov function special attention is given to the computational aspects of the approach and several methods to improve the computational efficiency are described

robust h filtering for networked systems with multiple state delays in this paper a new robust h filter design problem is studied for a class of networked systems with multiple state delays two kinds of incomplete measurements namely measurements with random delays and measurements with stochastic missing phenomenon are simultaneously considered such incomplete measurements are induced by the limited bandwidth of communication networks and are modelled as a linear function of a certain set of indicator functions that depend on the same stochastic variable attention is focused on the analysis and design problems of a full order robust h filter such that for all admissible parameter uncertainties and all possible incomplete measurements the filtering error dynamics is exponentially mean square stable and a prescribed h attenuation level is guaranteed some recently reported methodologies such as delay dependent and parameter dependent stability analysis approaches are employed to obtain less conservative results sufficient conditions which are dependent