learning overcomplete representations in an overcomplete basis the number of basis vectors is greater than the dimensionality of the input and the representation of an input is not a unique combination of basis vectors overcomplete representations have been advocated because they have greater robustness in the presence of noise can be sparser and can have greater flexibility in matching structure in the data overcomplete codes have also been proposed as a model of some of the response properties of neurons in primary visual cortex previous work has focused on finding the best representation of a signal using a fixed overcomplete basis or dictionary we present an algorithm for learning an overcomplete basis by viewing it as probabilistic model of the observed data we show that overcomplete bases can yield a better approximation of the underlying statistical distribution of the data and can thus lead to greater coding efficiency this can be viewed as a generalization of the technique of independent component analysis and provides a method for bayesian reconstruction of signals in the presence of noise and for blind source separation when there are more sources than mixtures

generalized hilbert operator on the dirichlet type space abstract the boundedness of the generalized hilbert operator on the dirichlet type space s p when 0 p 1 is investigated

the sharp lipschitz constants for feasible and optimal solutions of a perturbed linear program abstract the purpose of this paper is to derive the sharp lipschitz constants for the feasible solutions and optimal solutions of a linear program with respect to right hand side perturbations the lipschitz constants are given in terms of pseudoinverses of submatrices of the matrices involved and are proven to be sharp

an error entropy minimization algorithm for supervised training of nonlinear adaptive systems the paper investigates error entropy minimization in adaptive systems training we prove the equivalence between minimization of error s renyi 1970 entropy of order spl alpha and minimization of a csiszar 1981 distance measure between the densities of desired and system outputs a nonparametric estimator for renyi s entropy is presented and it is shown that the global minimum of this estimator is the same as the actual entropy the performance of the error entropy minimization criterion is compared with mean square error minimization in the short term prediction of a chaotic time series and in nonlinear system identification

lipschitz continuity of solutions of linear inequalities programs and complementarity problems it is shown that solutions of linear inequalities linear programs and certain linear complementarity problems e g those with p matrices or z matrices but not semidefinite matrices are lipschitz continuous with respect to changes in the right hand side data of the problem solutions of linear programs are not lipschitz continuous with respect to the coefficients of the objective function the lipschitz constant given here is a generalization of the role played by the norm of the inverse of a nonsingular matrix in bounding the perturbation of the solution of a system of equations in terms of a right hand side perturbation

an arma robust system identification using a generalized l sub p norm estimation algorithm the parameter estimation of arma systems with noisy output data is considered the system output is corrupted by measurement noise and the noise distribution is assumed to be unknown a generalized l sub p norm iterative estimation algorithm 1

cesaro type operators on some spaces of analytic functions on the unit ball abstract let h b denote the space of all holomorphic functions on the unit ball b c n in this paper we investigate the following integral operators t g f z 0 1 f tz r g tz dt t and l g f z 0 1 r f tz g tz dt t where f h b z b g h b and r h z j 1 n z j h z j z is the radial derivative of h the operator t g can be considered as an extension of the cesaro operator on the unit disk the boundedness and compactness of the operators t g and l g on the zygmund space and from the zygmund space to the bloch space are studied

enhancing the resolution of the spectrogram based on a simple adaptation procedure this work is concerned with improving the quality of signal localization for the short time fourier transform by properly adjusting the size of its analysis window over time the adaptation procedure involves the estimation of an area in the time frequency plane which is more compact than the support of the fixed window spectrogram then at each time instant the optimal window is selected such that the signal energy is maximized within the identified area the proposed method achieves its objectives and can compare favorably with alternative time adaptive spectrograms as well as with advanced quadratic representations

a conjugate gradient method for the unconstrained minimization of strictly convex quadratic splines in this paper we show that an analogue of the classical conjugate gradient method converges linearly when applied to solving the problem of unconstrained minimization of a strictly convex quadratic spline since a strictly convex quadratic program with simple bound constraints can be reformulated as unconstrained minimization of a strictly convex quadratic spline the conjugate gradient method is used to solve the unconstrained reformulation and find the solution of the original quadratic program in particular if the solution of the original quadratic program is nondegenerate then the conjugate gradient method finds the solution in a finite number of iterations

on an integral type operator between weighted type spaces and bloch type spaces on the unit ball abstract the boundedness and compactness of an integral type operator which has been recently introduced by the first author between weighted type spaces and bloch type spaces on the unit ball are studied here

hankel matrices acting on dirichlet spaces abstract we give a connection between the hankel matrix acting on dirichlet spaces d 0 2 and the carleson measure supported on 1 1 as an application we prove that the generalized hilbert operators h are always bounded on dirichlet spaces d for 0 2 and that the range 0 2 of in our results is the best possible

a cooperative recurrent neural network for solving l1 estimation problems with general linear constraints the constrained l1 estimation is an attractive alternative to both the unconstrained l1 estimation and the least square estimation in this letter we propose a cooperative recurrent neural network crnn for solving l1 estimation problems with general linear constraints the proposed crnn model combines four individual neural network models automatically and is suitable for parallel implementation as a special case the proposed crnn includes two existing neural networks for solving unconstrained and constrained l1 estimation problems respectively unlike existing neural networks with penalty parameters for solving the constrained l1 estimation problem the proposed crnn is guaranteed to converge globally to the exact optimal solution without any additional condition compared with conventional numerical algorithms the proposed crnn has a low computational complexity and can deal with the l1 estimation problem with degeneracy several applied examples show that the proposed crnn can obtain more accurate estimates than several existing algorithms

an adaptive optimal kernel time frequency representation time frequency representations with fixed windows or kernels figure prominently in many applications but perform well only for limited classes of signals representations with signal dependent kernels can overcome this limitation however while they often perform well most existing schemes are block oriented techniques unsuitable for on line implementation or for tracking signal components with characteristics that change with time the time frequency representation developed in the present paper based on a signal dependent radially gaussian kernel that adapts over time surmounts these difficulties the method employs a short time ambiguity function both for kernel optimization and as an intermediate step in computing constant time slices of the representation careful algorithm design provides reasonably efficient computation and allows on line implementation certain enhancements such as cone kernel constraints and approximate retention of marginals are easily incorporated with little additional computation while somewhat more expensive than fixed kernel representations this new technique often provides much better performance several examples illustrate its behavior on synthetic and real world signals

reduced dimension blind space time 2 d rake receivers for ds cdma communication systems blind space time rake receivers for classical ds cdma that cancel strong multiuser access interference while optimally combining the desired user s multipath are presented the delay spread is assumed to be fraction of a symbol interval as in the is 95 cdma standard the post correlation symbol interval is segmented into that which encompasses the rake fingers and that away from the rake fingers the signal plus interference and interference alone space time correlation matrices are estimated during the former and latter respectively reduced dimension space time rake receivers are investigated for alleviation of the computational burden of computing the largest generalized eigenvector of the resulting matrix pencil theoretical analysis and supporting simulations reveal that if the space and time compressing transformations are designed judiciously reduced dimension two dimensional 2 d rake receivers offer faster convergence at the expense of only a slight loss in asymptotic signal to interference plus noise ratio sinr relative to the full dimension space time rake receiver applications to the is 95 uplink are also presented along with supporting simulations

discrete time neural network for fast solving large linear l_ 1 estimation problems and its application to image restoration there is growing interest in solving linear l1 estimation problems for sparsity of the solution and robustness against non gaussian noise this paper proposes a discrete time neural network which can calculate large linear l1 estimation problems fast the proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution then the proposed neural network is efficiently applied to image restoration numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear l1 estimation problems but also needs much less computational time than the related algorithms in solving both linear l1 estimation and image restoration problems

improved instantaneous frequency estimation using an adaptive short time fourier transform instantaneous frequency estimation ife arises in a variety of important applications including fm demodulation we present here a new time frequency representation tfr based approach to ife based on an adaptive short time fourier transform astft this tfr leads naturally to a type of short term ml estimator of the if to further improve the performance we apply a multistate hidden markov model hmm based post estimation tracker the end result is up to a 16 db reduction in the threshold snr over the frequency discriminator fd and an 8 db improvement over the phase locked loop pll for a rayleigh fading channel

general parameterized time frequency transform interest in parameterized time frequency analysis for non stationary signal processing is increasing steadily an important advantage of such analysis is to provide highly concentrated time frequency representation with signal dependent resolution in this paper a general scheme named as general parameterized time frequency transform gptf transform is proposed for carrying out parameterized time frequency analysis the gptf transform is defined by applying generalized kernel based rotation operator and shift operator it provides the availability of a single generalized time frequency transform for applications on signals of different natures furthermore by replacing kernel function it facilitates the implementation of various parameterized time frequency transforms from the same standpoint the desirable properties and the dual definition in the frequency domain of gptf transform are also described in this paper one of the advantages of the gptf transform is that the generalized kernel can be customized to characterize the time frequency signature of non stationary signal as different kernel formulation has bias toward the signal to be analyzed a proper kernel is vital to the gptf thus several potential kernels are provided and discussed in this paper to develop the desired parameterized time frequency transforms in real applications it is desired to identify proper kernel with respect to the considered signal this motivates us to propose an effective method to identify the kernel for the gptf

a nonnegative pca algorithm for independent component analysis we consider the task of independent component analysis when the independent sources are known to be nonnegative and well grounded so that they have a nonzero probability density function pdf in the region of zero we propose the use of a nonnegative principal component analysis nonnegative pca algorithm which is a special case of the nonlinear pca algorithm but with a rectification nonlinearity and we conjecture that this algorithm will find such nonnegative well grounded independent sources under reasonable initial conditions while the algorithm has proved difficult to analyze in the general case we give some analytical results that are consistent with this conjecture and some numerical simulations that illustrate its operation

nonsmooth neural network for convex time dependent constraint satisfaction problems this paper introduces a nonsmooth ns neural network that is able to operate in a time dependent td context and is potentially useful for solving some classes of ns td problems the proposed network is named nonsmooth time dependent network ntn and is an extension to a td setting of a previous ns neural network for programming problems suppose c t t ge 0 is a nonempty td convex feasibility set defined by td inequality constraints the constraints are in general ns nondifferentiable functions of the state variables and time ntn is described by the subdifferential with respect to the state variables of an ns td barrier function and a vector field corresponding to the unconstrained dynamics this paper shows that for suitable values of the penalty parameter the ntn dynamics displays two main phases in the first phase any solution of ntn not starting in c 0 at t 0 is able to reach the moving set c cdot in finite time t h whereas in the second phase the solution tracks the moving set i e it stays within c t for all subsequent times t ge t h ntn is thus able to find an exact feasible solution in finite time and also to provide an exact feasible solution for subsequent times this new and peculiar dynamics displayed by ntn is potentially useful for addressing some significant td signal processing tasks as an illustration this paper discusses a number of examples where ntn is applied to the solution of ns td convex feasibility problems

extended cesaro operators between bloch type spaces in the unit ball of cn abstract in this paper we characterize those holomorphic symbols in the unit ball of c n for which the induced extended cesaro operator t b b respectively b 0 b 0 is bounded or compact where and are normal functions on 0 1 in addition we obtain some properties of those spaces b and b 0

undamped oscillations generated by hopf bifurcations in fractional order recurrent neural networks with caputo derivative in this paper a fractional order recurrent neural network is proposed and several topics related to the dynamics of such a network are investigated such as the stability hopf bifurcations and undamped oscillations the stability domain of the trivial steady state is completely characterized with respect to network parameters and orders of the commensurate order neural network based on the stability analysis the critical values of the fractional order are identified where hopf bifurcations occur and a family of oscillations bifurcate from the trivial steady state then the parametric range of undamped oscillations is also estimated and the frequency and amplitude of oscillations are determined analytically and numerically for such commensurate order networks meanwhile it is shown that the incommensurate order neural network can also exhibit a hopf bifurcation as the network parameter passes through a critical value which can be determined exactly the frequency and amplitude of bifurcated oscillations are determined

stft with adaptive window width based on the chirp rate an adaptive time frequency representation tfr with higher energy concentration usually requires higher complexity recently a low complexity adaptive short time fourier transform astft based on the chirp rate has been proposed to enhance the performance this method is substantially modified in this paper i because the wavelet transform used for instantaneous frequency if estimation is not signal dependent a low complexity astft based on a novel concentration measure is addressed ii in order to increase robustness to if estimation error the principal component analysis pca replaces the difference operator for calculating the chirp rate and iii a more robust gaussian kernel with time frequency varying window width is proposed simulation results show that our method has higher energy concentration than the other astfts especially for multicomponent signals and nonlinear fm signals also for if estimation our method is superior to many other adaptive tfrs in low signal to noise ratio snr environments

cooperative recurrent neural networks for the constrained l_ 1 estimator the constrained least absolute deviation l1 estimator is an attractive alternative to both the unconstrained l1 estimator and the least square estimator this paper introduces a constrained l1 method and proposes two cooperative recurrent neural networks crnns for the constrained l1 estimator unlike existing cooperative neural networks the proposed two crnns have a novel weighting cooperation scheme to integrate individual neural network information automatically as a special case the proposed continuous time crnn includes the existing continuous time neural network for unconstrained l1 estimator compared with existing continuous time neural networks for the constrained l1 estimator the proposed continuous time crnn has a lower model complexity and the finite time convergence to the exact optimal solution without any additional condition furthermore compared with existing numerical algorithms for the constrained l1 estimator in addition to a low computational complexity the proposed two crnns are suitable for parallel implementation and can deal with the l1 estimation problem with degeneracy the proposed two crnns are applied to parameter estimation problems under non gaussian noise environments simulation results demonstrate that the proposed crnns are indeed effective in dealing with the l1 estimation problem with nonunique solutions and in obtaining a better solution than relevant algorithms

products of volterra type operator and composition operator from h and bloch spaces to zygmund spaces the boundedness and compactness of the products of volterra type operators and composition operators from the space of bounded analytic functions and the bloch space to the zygmund space are discussed in this paper

time frequency representation based on an adaptive short time fourier transform in this paper a new concise algorithm about time frequency representation tfr based on an adaptive short time fourier transform astft is presented in this algorithm the analysis window width is equal to the local stationary length which is measured by the instantaneous frequency gradient ifg of the signal and the instantaneous frequency if of the signal is obtained by detecting the ridge of wavelet transform wt the astft provides much better performance than conventional tfr algorithms furthermore the algorithm is simpler and more computational efficient than some of other adaptive tfr algorithms proposed previously several examples are presented to illustrate its behavior on different kinds of signals and demonstrate its validity

l sub 1 and l sub infinity minimization via a variant of karmarkar s algorithm simple iterative algorithms are presented for l sub 1 and l sub infinity minimization regression based on a variant of karmarkar s linear programming algorithm although these algorithms are based on entirely different theoretical principles to the popular irls iteratively reweighted least squares algorithm they have almost identical matrix operations also presented are the results of a monte carlo study comparing the numerical convergence properties of the karmarkar algorithm for l sub 1 minimization to those of an irls and a simplex algorithm the test problem involves l sub 1 estimation of ar autoregressive model parameters the karmarkar algorithm outperformed irls by achieving higher numerical accuracy in fewer iterations techniques for reducing the computational cost per iteration of the karmarkar l sub 1 algorithm are discussed

image restoration by convex projections using adaptive constraints and the l sub 1 norm a new projection method based on the row action projection rap algorithm for image restoration is presented the new implementation is computationally attractive and facilitates local adaption of projection operators the local mean and the least l sub 1 norm set of solutions are used as constraints computer simulations illustrate the new methods to be very competitive in restoring missing spectral components of a degraded image

products of integral type operators and composition operators between bloch type spaces a complete picture on the boundedness and compactness of the products of integral type operators and composition operators between bloch type spaces of holomorphic functions on the unit disk is given in this paper

a four parameter atomic decomposition of chirplets a new four parameter atomic decomposition of chirplets is developed for compact and precise representation of signals with chirp components the four parameter chirplet atom is obtained from the unit gaussian function by successive applications of scaling fractional fourier transform frft and time shift and frequency shift operators the application of the frft operator results in a rotation of the wigner distribution of the gaussian in the time frequency plane by a specified angle the decomposition is realized by using the matching pursuit algorithm for this purpose the four parameter space is discretized to obtain a small but complete subset in the hilbert space a time frequency distribution tfd is developed for clear and readable visualization of the signal components it is observed that the chirplet decomposition and the related tfd provide more compact and precise representation of signal inner structures compared with the commonly used time frequency representations

generalized composition operators on zygmund spaces and bloch type spaces the boundedness and compactness of the generalized composition operator on zygmund spaces and bloch type spaces are investigated in this paper

on a new integral type operator from the bloch space to bloch type spaces on the unit ball abstract we introduce the following integral type operator on the space h b of all holomorphic functions on the unit ball b c n p g f z 0 1 f t z g t z d t t z b where g h b g 0 0 and is a holomorphic self map of b the boundedness and compactness of the operator from the bloch space b or the little bloch space b 0 to the bloch type space b or the little bloch type space b 0 are characterized in the main results we calculate the essential norm of the operators p g b or b 0 b or b 0 in an elegant way

performance analysis of minimum spl lscr sub 1 norm solutions for underdetermined source separation results of the analysis of the performance of minimum spl lscr sub 1 norm solutions in underdetermined blind source separation that is separation of n sources from m n linearly mixed observations are presented in this paper the minimum spl lscr sub 1 norm solutions are known to be justified as maximum a posteriori probability map solutions under a laplacian prior previous works have not given much attention to the performance of minimum spl lscr sub 1 norm solutions despite the need to know about its properties in order to investigate its practical effectiveness we first derive a probability density of minimum spl lscr sub 1 norm solutions and some properties we then show that the minimum spl lscr sub 1 norm solutions work best in a case in which the number of simultaneous nonzero source time samples is less than the number of sensors at each time point or in a case in which the source signals have a highly peaked distribution we also show that when neither of these conditions is satisfied the performance of minimum spl lscr sub 1 norm solutions is almost the same as that of linear solutions obtained by the moore penrose inverse our results show when the minimum spl lscr sub 1 norm solutions are reliable

neural data fusion algorithms based on a linearly constrained least square method two novel neural data fusion algorithms based on a linearly constrained least square lcls method are proposed while the lcls method is used to minimize the energy of the linearly fused information two neural network algorithms are developed to overcome the ill conditioned and singular problems of the sample covariance matrix arisen in the lcls method the proposed neural fusion algorithms are samples for implementation using both software and hardware compared with the existing fusion methods the proposed neural data fusion method has an unbiased statistical property and does not require any a priori knowledge about the noise covariance it is shown that the proposed neural fusion algorithms converge globally to the optimal fusion solution when the sample covariance matrix is singular and converge globally with exponential rate when the sample covariance matrix is nonsingular we apply the proposed neural fusion method to image and signal fusion and it is shown that the quality of the solution can be greatly enhanced by the proposed technique

characterization of signals by the ridges of their wavelet transforms the characterization and the separation of amplitude and frequency modulated signals is a classical problem of signal analysis and signal processing we present a couple of new algorithmic procedures for the detection of ridges in the modulus of the continuous wavelet transform of one dimensional 1 d signals these detection procedures are shown to be robust to additive white noise we also derive and test a new reconstruction procedure the latter uses only information from the restriction of the wavelet transform to a sample of points from the ridge this provides a very efficient way to code the information contained in the signal

system parameter estimation with input output noisy data and missing measurements an investigation is undertaken to examine the parameter estimation problem of linear systems when some of the measurements are unavailable i e missing data and the probability of occurrence of missing data is unknown a priori the system input and output data are also assumed to be corrupted by measurement noise and the knowledge of the noise distribution is unknown under the unknown noise distribution and missing measurements a consistent parameter estimation algorithm which is based on an l sub p norm iterative estimation algorithm iteratively reweighted least squares irls is proposed to estimate the system parameters we show that if the probability of missing measurement is less than one half the parameter estimates via the proposed estimation algorithm will converge to the true parameters as the number of data tends to infinity finally several simulation results are presented to illustrate the performance of the proposed l sub p norm iterative estimation algorithm simulation results indicate that under input output missing data and noise environment the proposed parameter estimation algorithm is an efficient approach toward the system parameter estimation problem

extended cesaro operators between different hardy spaces abstract let h p denote the hardy space of holomorphic functions on the unit ball b this note gives some sufficient and necessary conditions for the boundedness and compactness of the following extended cesaro operators t g f z 0 1 f tz r g tz dt t and l g f z 0 1 r f tz g tz dt t where z b and g is a fixed holomorphic map on b acting from the space h p into the space h q for the case p q our results extend and simplify some one dimensional results

integral type operators between α bloch spaces and besov spaces on the unit ball abstract let h b denote the space of all holomorphic functions on the unit ball b c n the boundedness and compactness of the following integral type operators t g f z 0 1 f tz r g tz dt t and l g f z 0 1 r f tz g tz dt t z b where g h b and r h z is the radial derivative of h between bloch spaces and besov spaces on the unit ball are characterized the results regarding the case when these operators map bloch spaces to besov spaces are nontrivial generalizations of recent one dimensional results by li and the present author

the linear l 1 estimator and the huber m estimator relationships between a linear l1 estimation problem and the huber m estimator problem can be easily established by their dual formulations the least norm solution of a linear programming problem studied by mangasarian and meyer siam j control optim 17 1979 pp 745 752 provides a key link between the dual problems based on the dual formulations we establish a local linearity property of the huber m estimators with respect to the tuning parameter gamma and prove that the solution set of the huber m estimator problem is lipschitz continuous with respect to perturbations of the tuning parameter gamma as a consequence the set of the linear l1 estimators is the limit of the set of the huber m estimators as gamma to 0 thus the huber m estimator problem has many solutions for small tuning parameter gamma if the linear l1 estimation problem has multiple solutions a recursive version of madsen and nielsen s algorithm siam j optim 3 1993 pp 223 235 based on computation of the huber m estimator is proposed for finding a linear l1 estimator

asymptotic wavelet and gabor analysis extraction of instantaneous frequencies the behavior of the continuous wavelet and gabor coefficients in the asymptotic limit using stationary phase approximations are investigated in particular it is shown how under some additional assumptions these coefficients allow the extraction of some characteristics of the analyzed signal such as frequency and amplitude modulation laws applications to spectral line estimations and matched filtering are briefly discussed

gradient projection network analog solver for linearly constrained nonlinear programming an analog approach is presented for solving nonlinear programming problems with linear constraint conditions the present method is based on transformation of variables with exponential functions which enables every trajectory to pass through an interior of feasible regions along a gradient direction projected onto the feasible space convergence of its trajectory to the solution of optimization problems is guaranteed and it is shown that the present scheme is an extension of the affine scaling method for linear programming to nonlinear programs under a slight modification of riemannian metric an analog electronic circuit is also presented that implements the proposed scheme in real time

fast directional spatially localized spherical harmonic transform we propose a transform for signals defined on the sphere that reveals their localized directional content in the spatio spectral domain when used in conjunction with an asymmetric window function we call this transform the directional spatially localized spherical harmonic transform directional slsht which extends the slsht from the literature whose usefulness is limited to symmetric windows we present an inversion relation to synthesize the original signal from its directional slsht distribution for an arbitrary window function as an example of an asymmetric window the most concentrated band limited eigenfunction in an elliptical region on the sphere is proposed for directional spatio spectral analysis and its effectiveness is illustrated on the synthetic and mars topographic data sets finally since such typical data sets on the sphere are of considerable size and the directional slsht is intrinsically computationally demanding depending on the band limits of the signal and window a fast algorithm for the efficient computation of the transform is developed the floating point precision numerical accuracy of the fast algorithm is demonstrated and a full numerical complexity analysis is presented

neural network for solving extended linear programming problems a neural network for solving extended linear programming problems is presented and is shown to be globally convergent to exact solutions the proposed neural network only uses simple hardware in which no analog multiplier for variables is required and has no parameter tuning problem finally an application of the neural network to the l sub 1 norm minimization problem is given

integral type operators acting between weighted type spaces on the unit ball let b denote the unit ball in c n and h b the space of all holomorphic functions on b we study the boundedness and compactness of the following integral type operatorsi f gf z email protected 0 1rf f tz g tz dtt email protected b where email protected h b g 0 0 f is a holomorphic self map of b and rf is the radial derivative of f between weighted type spaces on the unit ball

a generalized hopfield network for nonsmooth constrained convex optimization lie derivative approach this paper proposes a generalized hopfield network for solving general constrained convex optimization problems first the existence and the uniqueness of solutions to the generalized hopfield network in the filippov sense are proved then the lie derivative is introduced to analyze the stability of the network using a differential inclusion the optimality of the solution to the nonsmooth constrained optimization problems is shown to be guaranteed by the enhanced fritz john conditions the convergence rate of the generalized hopfield network can be estimated by the second order derivative of the energy function the effectiveness of the proposed network is evaluated on several typical nonsmooth optimization problems and used to solve the hierarchical and distributed model predictive control four tank benchmark

a new regression estimator with neural network realization a new regression estimator viewed as the solution of a strictly convex quadratic programming problem is introduced in this paper two recurrent neural networks in continuous time and discrete time respectively are proposed to solve the quadratic programming problem in real time the continuous time neural network is shown to have a global stability including the global asymptotic and exponential stability the discrete time neural network is shown to have a global convergence with a fixed step length this fixed step length can be independent of the regression problem size by scaling a design parameter since the sizes of the proposed neural networks depend only on the constraints of the optimization problems the proposed new regression estimator can obtained by two novel neural networks with lower implementation costs than the conventional methods our simulation results confirm that the proposed neural networks are effective in solving various kind of regression problems

a recursive least squares implementation for lcmp beamforming under quadratic constraint quadratic constraints on the weight vector of an adaptive linearly constrained minimum power lcmp beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters we propose a technique for implementing a quadratic inequality constraint with recursive least squares rls updating a variable diagonal loading term is added at each step where the amount of loading has a closed form solution simulations under different scenarios demonstrate that this algorithm has better interference suppression than both the rls beamformer with no quadratic constraint and the rls beamformer using the scaled projection technique as well as faster convergence than lms beamformers

a compact cooperative recurrent neural network for computing general constrained l_1 norm estimators recently cooperative recurrent neural networks for solving three linearly constrained l 1 estimation problems were developed and applied to linear signal and image models under non gaussian noise environments for wide applications this paper proposes a compact cooperative recurrent neural network crnn for calculating general constrained l 1 norm estimators it is shown that the proposed crnn converges globally to the constrained l 1 norm estimator without any condition the proposed crnn includes three existing crnns as its special cases unlike the three existing crnns the proposed crnn is easily applied and can deal with the nonlinear elliptical sphere constraint moreover when computing the general constrained l 1 norm estimator the proposed crnn has a fast convergence speed due to low computational complexity simulation results confirm further the good performance of the proposed crnn

on a new operator from the logarithmic bloch space to the bloch type space on the unit ball abstract we study the boundedness and compactness of a recently introduced operator denoted by p g which is a kind of the product of composition and integral operators on the unit ball b c n from the logarithmic bloch space b log and the little logarithmic bloch space b log 0 to the bloch type space b or the little bloch type space b 0

use of the cross wigner ville distribution for estimation of instantaneous frequency an iterative instantaneous frequency if estimation scheme is presented in which successive if estimates are obtained from the peak of the cross wigner ville distribution xwvd using a reference signal synthesized from an initial if estimate theoretical and practical aspects of performance are discussed and the performance is compared with that of other methods

localization of the complex spectrum the s transform the s transform which is introduced in the present correspondence is an extension of the ideas of the continuous wavelet transform cwt and is based on a moving and scalable localizing gaussian window it is shown to have some desirable characteristics that are absent in the continuous wavelet transform the s transform is unique in that it provides frequency dependent resolution while maintaining a direct relationship with the fourier spectrum these advantages of the s transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis whereas the localizing scalable gaussian window dilates and translates