on the minimum euclidean distance for a class of signal space codes the minimum euclidean distance for a class of constant envelope phase modulation codes is studied bandwidth and power efficient signals with continuous phase are considered the information carrying phase varies piecewise linearly and the slopes are cyclically changed for successive symbol time intervals yielding the so called multi h signals it has previously been shown that this class of signals contains bandwidth and power efficient signals when coherent maximum likelihood sequence detection is used bounds on the achievable euclidean distance for signals in the above class are given upper bounds are calculated as well as minimum distance results for specific multilevel multi h signals it is concluded that quaternary and octal muiti h schemes considerably outperform the binary schemes furthermore in the important small modulation index region 2 h codes gain the maximum 3 db larger gains are not available by increasing the number of h values

dissipativity analysis for singular systems with time varying delays this paper is concerned with the dissipativity analysis problem for singular systems with time varying delays a delay dependent criterion is established to guarantee the dissipativity of the underlying systems using the delay partitioning technique all the results given in this paper are not only dependent upon the time delay but also dependent upon the number of delay partitions the effectiveness and the reduced conservatism of the derived results are demonstrated by two illustrative examples

an improved global asymptotic stability criterion for delayed cellular neural networks a new lyapunov krasovskii functional is constructed for delayed cellular neural networks and the s procedure is employed to handle the nonlinearities an improved global asymptotic stability criterion is also derived that is a generalization of and an improvement over previous results numerical examples demonstrate the effectiveness of the criterion

robust dissipative control for linear systems with dissipative uncertainty this paper focuses on the problem of quadratic dissipative control for linear systems with or without uncertainty we consider the design of feedback controllers to achieve robust asymptotic stability and strict quadratic dissipativeness both linear static state feedback and dynamic output feedback controllers are considered first the equivalence between strict quadratic dissipativeness of linear systems and a h performance is established necessary and sufficient conditions for the solution of the quadratic dissipative control problem are then obtained using a linear matrix inequality lmi approach as for uncertain systems we consider structured uncertainty characterized by a dissipative system this uncertainty description is quite general and contains commonly used types of uncertainty such as norm bounded and positive real uncertainties as special cases it is shown that the robust dissipative control problem can be solved in terms of a scaled quadratic dissipative control problem without un

a minimal parameter adaptive notch filter with constrained poles and zeros a new algorithm is presented for adaptive notch filtering and parametric spectral estimation of multiple narrow band or sine wave signals in an additive broad band process the algorithm is of recursive prediction error rpe form and uses a special constrained model of infinite impulse response iir with a minimal number of parameters the convergent filter is characterized by highly narrow bandwidth and uniform notches of desired shape for sufficiently large data sets the variances of the sine wave frequency estimates are of the same order of magnitude as the cramer rao bound results from simulations illustrate the performance of the algorithm under a wide range of conditions

a multistage reduction technique for feedback stabilizing distributed time lag systems abstract a direct pole relocation theory is advanced for linear time invariant systems with distributed delays in both state and control variables the principal tools of the theory include i the finite cardinality of the unstable spectrum ii a set of matrices each of which is a left zero of the system characteristic quasi polynomial matrix and iii a linear transformation which reduces the delay system to a sufficiently high order delay free system whose spectrum contains the delay system unstable spectrum it is shown that if the delay system is spectrally stabilizable then it shares a common feedback stabilizing control law with its delay free counterpart this point of contact with a delay free system permits the determination of the control law using well established ordinary system methods the workability of the approach hinges on the ability to partition the unstable spectrum augmented with additional poles from the stable spectrum if necessary into n symmetric sets when this partition is impossible a spectral controllability invariance theorem facilitates resolution of the problem

dissipativity analysis for neural networks with two delay components using an extended reciprocally convex matrix inequality abstract this paper focuses on the problem of strictly q s r dissipativity analysis for neural networks with two delay components based on the dynamic delay interval method a lyapunov krasovskii functional is constructed by solving its self positive definite and derivative negative definite conditions via an extended reciprocally convex matrix inequality several new sufficient conditions that guarantee the neural networks strictly q s r dissipative are derived furthermore the dissipativity analysis of neural networks with two delay components is extended to the stability analysis finally two numerical examples are employed to illustrate the advantages of the proposed method

stability analysis for neural networks with time varying interval delay this letter is concerned with the stability analysis of neural networks nns with time varying interval delay the relationship between the time varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of lyapunov functional as a result some improved delay interval dependent stability criteria for nns with time varying interval delay are proposed numerical examples are given to demonstrate the effectiveness and the merits of the proposed method

dissipativity analysis for discrete stochastic neural networks with markovian delays and partially known transition matrix the problem of dissipativity analysis for a class of discrete time stochastic neural networks with discrete and finite distributed delays is considered in this paper system parameters are described by a discrete time markov chain a discretized jensen inequality and lower bounds lemma are employed to reduce the number of decision variables and to deal with the involved finite sum quadratic terms in an efficient way a sufficient condition is derived to ensure that the neural networks under consideration is globally delay dependent asymptotically stable in the mean square and strictly z s g a dissipative next the case in which the transition probabilities of the markovian channels are partially known is discussed numerical examples are given to emphasize the merits of reduced conservatism of the developed results

exponential synchronization of a class of neural networks with sampled data control abstract this paper investigates the problem of the master slave synchronization for a class of neural networks with discrete and distributed delays under sampled data control by introducing some new terms a novel piecewise time dependent lyapunov krasovskii functional lkf is constructed to fully capture the available characteristics of real sampling information and nonlinear function vector of the system based on the lkf and wirtinger based inequality less conservative synchronization criteria are obtained to guarantee the exponential stability of the error system and then the slave system is synchronized with the master system the designed sampled data controller can be obtained by solving a set of linear matrix inequalities lmis which depend on the maximum sampling period and the decay rate the criteria are less conservative than the ones obtained in the existing works a numerical example is presented to illustrate the effectiveness and merits of the proposed method

a decomposition approach to cpm it is shown that any continuous phase modulation cpm system can be decomposed into a continuous phase encoder and a memoryless modulator in such a way that the former is a linear modulo some integer p time invariant sequential circuit and the latter is also time invariant this decomposition is exploited to obtain alternative realizations of the continuous phase encoder and hence of cpm and also to obtain alternative forms of the optimum decoding algorithm when p is a prime p so that the encoder is linear over the finite field gf p it is shown that cascading it with an outside convolutional encoder is equivalent to a single convolutional encoder it is pointed out that the cascade of the modulator the waveform channel which it is assumed is characterized by additive white gaussian noise and the demodulator that operates over one symbol interval yield a discrete memoryless channel that can be studied without the distractions introduced by continuous phase encoding

optimization of linear receivers for data communication signals the optimization of receivers constrained to consist of a linear filter and level slicer is considered for general data communication signals in additive white gaussian noise special consideration is given to the two limiting cases of arbitrarily low and high signal to noise ratios snrs the low snr case results in an average matched filter receiver the high snr filter is shown to be the solution to a specific nonlinear mathematical problem with a simple geometric interpretation an efficient algorithm is developed to solve the problem the results are illustrated for a duobinary minimum shift keyed signal observed over an interval of three symbol period duration

optimum parameter combinations for multi h phase codes it has been believed that the superiority of multi h phase codes over constant h continuous phase frequency shift keying cpfsk schemes is based entirely on the longer temporal separation of any two signal paths this assumption forces the number of states in the receiver to be lower bounded and requires a fast processor in the receiver counterexamples to this common belief are presented as well as results of a systematic search for good multi h codes for a given bandwidth and complexity constraints it is shown that quaternary multi h codes exist for which the first merge of the phase paths in the phase tree is not delayed as compared to the cpfsk scheme yet which still offer a significant coding gain over the corresponding cpfsk signals some quaternary and octal multi h codes are found with large minimum distance values and a smaller number of receiver states than those known previously

reliable mixed h passive control for t s fuzzy delayed systems based on a semi markov jump model approach abstract this paper investigates the problem of the reliable mixed h passive control for takagi sugeno t s fuzzy delayed systems based on a semi markov jump model smjm approach the focus is to design a fuzzy fault tolerant controller such that the resulting closed loop system is stochastically stable with a prescribed mixed h passive performance level even if the actuator failures appear a semi markov process is employed to describe the encountered failures of the actuator by applying the lyapunov krasovskii method in combination with some novel inequalities some conditions on the performance analysis are established where some negative quadratic terms are fully considered to reduce the conservatism based on the conditions an explicit expression for the desired controller is given three numerical examples are presented to show the effectiveness and reduced conservatism of the proposed method

delay dependent stability criteria for generalized neural networks with two delay components this paper investigates the delay dependent stability for generalized continuous neural networks with time varying delays a novel lyapunov krasovskii functional lkf that considers more information on activation functions of delayed neural networks and delay upper bounds is developed simultaneously most commonly used techniques for treating the derivative of the lkf are reviewed and compared with each other with the way of introducing slack matrices those techniques are classified into two categories including free weighting matrix fwm based techniques and reciprocally convex combination based techniques it is found that the introduced slack matrices play an important role in conservatism reducing and those four types of fwm based methods lead to same results and are equivalent moreover the obtained criteria are extended to the system with a single time varying delay two numerical examples are given to verify the effectiveness of the proposed method

tracking characteristics of the constrained iir adaptive notch filter the tracking behavior of a constrained iir notch filter the coefficients of which are estimated using a constant step size gauss newton algorithm is studied using weak convergence theory and the concept of prescaling it is shown that the mean behavior of the adaptive filter coefficients can be studied by using an ordinary differential equation ode approximate and simple closed form results are derived for the tracking behavior of a second order notch filter computer simulations are presented to substantiate the analysis

gradient based adaptive iir notch filtering for frequency estimation the use of gradient based algorithms with infinite impulse response iir notch filtering for estimating sinusoids imbedded in noise is investigated two notch filter model structures are presented the first is for applications where two signal sources with correlated noise components can be assessed the second can be used in situations where only one composite signal source is available error surface analysis indicates that second order modules are unimodal and result in guaranteed convergence higher order modules are multimodal and require judicious choice of initial parameter estimates simulation results are included to demonstrate the performance characteristics of both filter structures

delay dependent robust dissipativity conditions for delayed neural networks with random uncertainties this paper deals with the problem of the robust dissipativity analysis for delayed neural networks with randomly occurring uncertainties the randomly occurring uncertainties under consideration are assumed to follow certain mutually uncorrelated bernoulli distributed white noise sequences by using reciprocally convex approach combined with an extended wirtinger inequality some delay dependent conditions for the concerned neural networks to be stochastically strictly q s r dissipative are established finally two numerical examples are given to illustrate the reduced conservatism and effectiveness of our proposed approach

combined coded multi h cpfsk signaling a continuous phase frequency shift keying cpfsk signaling technique is suggested that combines convolutional encoding and multi h signaling in contrast to regular multi h signaling this technique changes the modulation index in a preselected pattern in order to maximize the minimum euclidean distance a rate 1 2 convolutional encoder along with a 2 h quaternary cpfsk modulator which uses two fixed modulation indexes is considered minimum euclidean distances are calculated corresponding to the best encoder mapper combinations for different modulation index patterns at attractive pairs of modulation indexes numerical results obtained for encoder memory lengths of one and two are used to illustrate that the minimum euclidean distance of coded cpfsk signals can be significantly increased by combining with multi h signaling modulation index patterns which perform significantly better than regular multi h signals are determined an error event analysis over the additive white gaussian noise channel is carried out to investigate the actual error rate performance and to verify the theoretical results

global asymptotical stability of recurrent neural networks with multiple discrete delays and distributed delays by employing the lyapunov krasovskii functional and linear matrix inequality lmi approach the problem of global asymptotical stability is studied for recurrent neural networks with both discrete time varying delays and distributed time varying delays some sufficient conditions are given for checking the global asymptotical stability of recurrent neural networks with mixed time varying delay the proposed lmi result is computationally efficient as it can be solved numerically using standard commercial software two examples are given to show the usefulness of the results

comparative study of four adaptive frequency trackers we study and compare four algorithms for adaptive retrieval of slowly time varying multiple cisoids in noise the adaptive notch filter the multiple frequency tracker the adaptive estimation scheme and the hyperstable adaptive line enhancer the local behavior of the algorithms in a neighborhood of their equilibrium state assuming high signal to noise ratio snr and large data sample for a two cisoid signal is treated in a similar way to the linear filter approximation technique used for a single cisoid case the validity of the results is confirmed by computer simulations

global asymptotic stability of reaction diffusion cohen grossberg neural networks with continuously distributed delays this paper is concerned with the global asymptotic stability of a class of reaction diffusion cohen grossberg neural networks with continuously distributed delays under some suitable assumptions and using a matrix decomposition method we apply the linear matrix inequality lmi method to propose some new sufficient stability conditions for the reaction diffusion cohen grossberg neural networks with continuously distributed delays the obtained results are easy to check and improve upon the existing stability results some remarks are given to show the advantages of the obtained results over the previous results an example is also given to demonstrate the effectiveness of the obtained results

correlatively encoded multi h signals a correlatively encoded multi h cpfsk signaling technique which performs significantly better than partial response multi h signaling is introduced numerical results presented for binary 2 h signaling indicate that correlatively encoded 2 h signals can achieve significant gains over both constant h signals and partial response 2 h signals

new results on dissipativity analysis of singular systems with time varying delay abstract this paper focuses on the dissipativity analysis of singular systems with time varying delay the objective is to obtain a less conservative criterion with less computational demand based on the state decomposition method singular systems are rewritten as differential subsystems and algebraic ones first of all the components of state vectors are applied to design a novel lyapunov krasovskii functional lkf for the differential subsystems then in the derivative of the lkf the algebraic ones are introduced and the wirtinger based integral inequality together with the extended reciprocally convex matrix inequality is adopted to estimate the single integral term therefore a new dissipativity criterion is obtained which realizes both less conservativeness and less computational demand finally two numerical examples are given to verify the superiority of the proposed method

global asymptotic stability for delayed neural networks using an integral inequality based on nonorthogonal polynomials this brief is concerned with global asymptotic stability of a neural network with a time varying delay first by introducing an auxiliary vector with some nonorthogonal polynomials a slack matrix based integral inequality is established which includes some existing one as its special case second a novel lyapunov krasovskii functional is constructed to suit for the use of the obtained integral inequality as a result a less conservative stability criterion is derived whose effectiveness is finally demonstrated through two well used numerical examples

passivity analysis for discrete time stochastic markovian jump neural networks with mixed time delays in this paper passivity analysis is conducted for discrete time stochastic neural networks with both markovian jumping parameters and mixed time delays the mixed time delays consist of both discrete and distributed delays the markov chain in the underlying neural networks is finite piecewise homogeneous by introducing a lyapunov functional that accounts for the mixed time delays a delay dependent passivity condition is derived in terms of the linear matrix inequality approach the case of markov chain with partially unknown transition probabilities is also considered all the results presented depend upon not only discrete delay but also distributed delay a numerical example is included to demonstrate the effectiveness of the proposed methods

stability analysis of distributed delay neural networks based on relaxed lyapunov krasovskii functionals this paper revisits the problem of asymptotic stability analysis for neural networks with distributed delays the distributed delays are assumed to be constant and prescribed since a positive definite quadratic functional does not necessarily require all the involved symmetric matrices to be positive definite it is important for constructing relaxed lyapunov krasovskii functionals which generally lead to less conservative stability criteria based on this fact and using two kinds of integral inequalities a new delay dependent condition is obtained which ensures that the distributed delay neural network under consideration is globally asymptotically stable this stability criterion is then improved by applying the delay partitioning technique two numerical examples are provided to demonstrate the advantage of the presented stability criteria

dissipativity analysis for discrete time stochastic neural networks with time varying delays in this paper the problem of dissipativity analysis is discussed for discrete time stochastic neural networks with time varying discrete and finite distributed delays the discretized jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly q s r y dissipative which is delay dependent in the sense that it depends on not only the discrete delay but also the finite distributed delay based on the dissipativity criterion some special cases are also discussed compared with the existing ones the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables three examples are given to illustrate the effectiveness and benefits of our theoretical results

on extended dissipativity of discrete time neural networks with time delay in this brief the problem of extended dissipativity analysis for discrete time neural networks with time varying delay is investigated the definition of extended dissipativity of discrete time neural networks is proposed which unifies several performance measures such as the h infty performance passivity l 2 l infty performance and dissipativity by introducing a triple summable term in lyapunov function the reciprocally convex approach is utilized to bound the forward difference of the triple summable term and then the extended dissipativity criterion for discrete time neural networks with time varying delay is established the derived condition guarantees not only the extended dissipativity but also the stability of the neural networks two numerical examples are given to demonstrate the reduced conservatism and effectiveness of the obtained results

a structural view of asymptotic convergence speed of adaptive iir filtering algorithms i infinite precision implementation for pt i see ibid vol 41 no 4 p 1493 1517 1993 finite precision fp implementation is the ultimately inevitable reality of all adaptive filters including adaptive infinite impulse response iir filters this paper continues to examine the asymptotic convergence speed of adaptive iir filters of various structures and algorithms including the simple constant gain type and the newton type but under fp implementation a stochastic differential equation sde approach is used in the analysis such an approach not only greatly simplifies the fp analysis which is traditionally very involved algebraically but it also provides valuable information about the first order as well as the second order moments that the latter are not available using the ordinary differential equation ode approach the asymptotic convergence speed as well as the convergent values of the pertinent moments of fp errors are examined in terms of unknown system pole zero locations the adverse effects of lightly damped low frequency ldlf poles resulting from fast sampling on the local transient and convergent behavior of various structures and algorithms are analyzed and compared the new results agree with the existing ones when reduced to the finite impulse response fir case in particular the explosive behavior of pertinent error variances of newton type iir algorithms when the forgetting factor spl lambda 1 is also concluded computer simulation verifies the predicted theoretical results

extended dissipative state estimation for markov jump neural networks with unreliable links this paper is concerned with the problem of extended dissipativity based state estimation for discrete time markov jump neural networks nns where the variation of the piecewise time varying transition probabilities of markov chain is subject to a set of switching signals satisfying an average dwell time property the communication links between the nns and the estimator are assumed to be imperfect where the phenomena of signal quantization and data packet dropouts occur simultaneously the aim of this paper is to contribute with a markov switching estimator design method which ensures that the resulting error system is extended stochastically dissipative in the simultaneous presences of packet dropouts and signal quantization stemmed from unreliable communication links sufficient conditions for the solvability of such a problem are established based on the derived conditions an explicit expression of the desired markov switching estimator is presented finally two illustrated examples are given to show the effectiveness of the proposed design method

a stable and efficient adaptive notch filter for direct frequency estimation we investigate the problem of direct frequency estimation a new adaptive algorithm is developed for constrained pole zero notch filters the basic characteristic of this algorithm is that the notch filter is cascaded and hence purely parametrized by the notching frequencies this makes the algorithm much simpler and hence more efficient and more robust such that the poles and zeros are automatically located on the respective constrained circle no matter if the algorithm is implemented with infinite or finite precision the algorithm is analyzed and compared with some existing algorithms several numerical examples are given as well as the corresponding simulations

event triggered state estimation for discrete time multidelayed neural networks with stochastic parameters and incomplete measurements in this paper the event triggered state estimation problem is investigated for a class of discrete time multidelayed neural networks with stochastic parameters and incomplete measurements in order to cater for more realistic transmission process of the neural signals we make the first attempt to introduce a set of stochastic variables to characterize the random fluctuations of system parameters in the addressed neural network model the delays among the interconnections are allowed to be different which are more general than those in the existing literature the incomplete information under consideration includes randomly occurring sensor saturations and quantizations for the purpose of energy saving an event triggered state estimator is constructed and a sufficient condition is given under which the estimation error dynamics is exponentially ultimately bounded in the mean square it is worth noting that the ultimate boundedness of the error dynamics is explicitly estimated the characterization of the desired estimator gain is designed in terms of the solution to a certain matrix inequality finally a numerical simulation example is presented to illustrate the effectiveness of the proposed event triggered state estimation scheme

some aspects of an adaptive digital notch filter with constrained poles and zeros the paper derives the range of filter parameters which can be used for monitoring purposes in adaptive notch filtering applications the nonconvergence problem associated with the stochastic gauss newton algorithm is illustrated and the paper proposes the use of a less efficient recursive estimation algorithm the appropriate maximum likelihood method to overcome this problem results from simulations are presented for demonstration purposes

observer based adaptive neural network control for nonlinear stochastic systems with time delay this paper considers the problem of observer based adaptive neural network nn control for a class of single input single output strict feedback nonlinear stochastic systems with unknown time delays dynamic surface control is used to avoid the so called explosion of complexity in the backstepping design process radial basis function nns are directly utilized to approximate the unknown and desired control input signals instead of the unknown nonlinear functions the proposed adaptive nn output feedback controller can guarantee all the signals in the closed loop system to be mean square semi globally uniformly ultimately bounded simulation results are provided to demonstrate the effectiveness of the proposed methods

stationary points of the recursive generalized least squares algorithm for adaptive notch filtering possible convergence points of the generalized least squares adaptive notch filtering algorithm are analytically derived for the multiple sinusoid case showing explicitly the dependence of the asymptotic bias on the pole contraction factor signal to noise ratio and the true model parameters the results for symmetric and nonsymmetric parameterization are compared

hierarchical stability conditions for a class of generalized neural networks with multiple discrete and distributed delays this brief investigates the analysis issue for global asymptotic stability of a class of generalized neural networks with multiple discrete and distributed delays to tackle delays arising in different neuron activation functions we employ a generalized model with multiple discrete and distributed delays which covers various existing neural networks we then generalize the bessel legendre inequalities to deal with integral terms with any linearly independent functions and nonlinear function of states based on these inequalities we design the lyapunov krasovskii functional and derive hierarchical linear matrix inequality stability conditions finally three numerical examples are provided to demonstrate that the proposed method is less conservative with a reasonable numerical burden than the existing results

exponential synchronization of neural networks with discrete and distributed delays under time varying sampling this paper investigates the problem of master slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals an improved method is proposed which captures the characteristic of sampled data systems some delay dependent criteria are derived to ensure the exponential stability of the error systems and thus the master systems synchronize with the slave systems the desired sampled data controller can be achieved by solving a set of linear matrix inequalitys which depend upon the maximum sampling interval and the decay rate the obtained conditions not only have less conservatism but also have less decision variables than existing results simulation results are given to show the effectiveness and benefits of the proposed methods

adaptive notch filtering for the retrieval of sinusoids in noise in this paper an adaptive notch filter is developed employing a frequency domain and time domain analysis for the enhancement and tracking of sinusoids in additive noise colored or white the notch filter is implemented as a constrained infinite impulse response filter with the constraint enforced by a single parameter termed the debiasing parameter the resulting notch filter requires few parameters facilitates the formation of the desired band rejection filter response and also leads to various useful implementations cascade parallel for the adaptation of the filter coefficients the stochastic gauss newton algorithm is used the convergence of this updating procedure is established by studying the associated differential equation also it is shown that the structure present in the problem enables truncation of the gradient thereby reducing the complexity of adapting the filter coefficients simulation results are presented to substantiate the analysis and to demonstrate the potential of the notch filtering technique

asymptotic stability of cellular neural networks with multiple proportional delays proportional delay is a kind of unbounded time varying delay which is different from unbounded distributed delays in this paper asymptotic stability of the equilibrium point of cellular neural networks with multiple proportional delays is presented sufficient conditions for delay dependent global asymptotic stability and delay independent uniform asymptotic stability of the system are obtained by employing matrix theory and constructing lyapunov functional two examples are given to illustrate the effectiveness of the obtained results and less conservative than previously existing results

stability analysis for delayed neural networks with an improved general free matrix based integral inequality this paper revisits the problem of stability analysis for neural networks with a time varying delay an improved general free matrix based fmb integral inequality is proposed with an undetermined number m compared with the conventional fmb ones the improved inequality involves a much smaller number of free matrix variables in particular the improved fmb integral inequality is expressed in a concrete form for any value of m by employing the new inequality with a properly constructed lyapunov krasovskii functional a new stability condition is derived for neural networks with a time varying delay two commonly used numerical examples are given to show strong competitiveness of the proposed approach in both the conservatism and computation burdens

new lyapunov krasovskii functionals for global asymptotic stability of delayed neural networks this brief deals with the problem of global asymptotic stability for a class of delayed neural networks some new lyapunov krasovskii functionals are constructed by nonuniformly dividing the delay interval into multiple segments and choosing proper functionals with different weighting matrices corresponding to different segments in the lyapunov krasovskii functionals then using these new lyapunov krasovskii functionals some new delay dependent criteria for global asymptotic stability are derived for delayed neural networks where both constant time delays and time varying delays are treated these criteria are much less conservative than some existing results which is shown through a numerical example

passivity criteria for continuous time neural networks with mixed time varying delays abstract this paper is concerned with the problem of passivity analysis for uncertain continuous time neural networks with mixed time varying delays the mixed time varying delays consist of both discrete and distributed delays in which the discrete delays are assumed to be varying within a given interval in addition the uncertainties are assumed to be norm bounded by employing a novel lyapunov krasovskii functional new passivity delay interval dependent criteria are established to guarantee the passivity performance when estimating an upper bound of the derivative of the lyapunov krasovskii functional we handle the terms related to the discrete and distributed delays appropriately so as to develop less conservative results these passivity conditions are presented in terms of linear matrix inequalities which can be easily solved via standard numerical software some numerical examples are given to illustrate the effectiveness of the proposed method

an adaptive iir structure for the separation enhancement and tracking of multiple sinusoids a new iir adaptive algorithm for separating enhancing and tracking multiple sinusoids is investigated in this algorithm up to m complex sinusoids are individually tracked by passing the input into a mth order iir notch filter and then into a parallel bank of m first order all pole filters whose poles are equal to the zeros of the notch filter the advantages of the algorithm are o m implementation complexity separation of the sinusoids one asymptotic time constant and inherent stability with poles constrained to be inside the unit circle

exponential stability on stochastic neural networks with discrete interval and distributed delays this brief addresses the stability analysis problem for stochastic neural networks snns with discrete interval and distributed time varying delays the interval time varying delay is assumed to satisfy 0 d1 d t d2 and is described as d t d 1 h t with 0 h t d 2 d 1 based on the idea of partitioning the lower bound d 1 new delay dependent stability criteria are presented by constructing a novel lyapunov krasovskii functional which can guarantee the new stability conditions to be less conservative than those in the literature the obtained results are formulated in the form of linear matrix inequalities lmis numerical examples are provided to illustrate the effectiveness and less conservatism of the developed results

minimum euclidean distance and power spectrum for a class of smoothed phase modulation codes with constant envelope a class of smoothed phase modulation codes is analyzed in terms of power spectral density and error probability for large signal to noise ratios the signals have constant envelope and the information carrying phase function is of multi h partial response continuous phase type a cyclic variation of modulation index from symbol time interval to symbol time interval is introduced coherent transmission and an additive white gaussian channel are assumed both power and bandwidth efficient signals are to be found in the above class comparisons are made between fixed h and multi h systems both for the binary and multilevel cases tradeoff is performed between bandwidth and error probability performance for large signal to noise ratios it is concluded that quaternary and octal systems always seem preferable increasing the number of modulation indexes from one in the fixed h case to two in the simplest multi h case gives performance advantages at the expense of increased system complexity

extended dissipativity analysis for discrete time delayed neural networks based on an extended reciprocally convex matrix inequality abstract in this paper the extended dissipativity analysis for discrete time neural networks with a time varying delay is investigated first a novel lyapunov krasovskii functional lkf is constructed with a delay product type term introduced then in the forward difference of the lkf the sum terms are bounded via an extended reciprocally convex matrix inequality as a result an extended dissipativity criterion is established in terms of linear matrix inequalities meanwhile this criterion is extended to the stability analysis of the counterpart system without disturbance finally two numerical examples are given to demonstrate the effectiveness and improvements of the presented criterion

adaptive recovery of a chirped sinusoid in noise ii performance of the lms algorithm the authors study the ability of the exponentially weighted recursive least square rls algorithm to track a complex chirped exponential signal buried in additive white gaussian noise power p sub n the signal is a sinusoid whose frequency is drifting at a constant rate psi lt is recovered using an m tap adaptive predictor five principal aspects of the study are presented the methodology of the analysis proof of the quasi deterministic nature of the data covariance estimate r k a new analysis of rls for an inverse system modeling problem a new analysis of rls for a deterministic time varying model for the optimum filter and an evaluation of the residual output mean square error mse resulting from the nonoptimality of the adaptive predictor the misadjustment in terms of the forgetting rate beta of the rls algorithm it is shown that the misadjustment is dominated by a lag term of order beta sup 2 and a noise term of order beta thus a value beta sub opt exists which yields a minimum misadjustment it is proved that beta sub opt m 1 rho psi sup 2 sup 1 3 and the minimum misadjustment is equal to 3 4 p sub n m 1 beta sub opt where rho is the input signal to noise ratio snr

stability and hopf bifurcation of a general delayed recurrent neural network in this paper stability and bifurcation of a general recurrent neural network with multiple time delays is considered where all the variables of the network can be regarded as bifurcation parameters it is found that hopf bifurcation occurs when these parameters pass through some critical values where the conditions for local asymptotical stability of the equilibrium are not satisfied by analyzing the characteristic equation and using the frequency domain method the existence of hopf bifurcation is proved the stability of bifurcating periodic solutions is determined by the harmonic balance approach nyquist criterion and graphic hopf bifurcation theorem moreover a critical condition is derived under which the stability is not guaranteed thus a necessary and sufficient condition for ensuring the local asymptotical stability is well understood and from which the essential dynamics of the delayed neural network are revealed finally numerical results are given to verify the theoretical analysis and some interesting phenomena are observed and reported

stability and dissipativity analysis of distributed delay cellular neural networks in this brief the problems of delay dependent stability analysis and strict q s dissipativity analysis are investigated for cellular neural networks cnns with distributed delay first by introducing an integral partitioning technique two new forms of lyapunov krasovskii functionals are constructed and improved distributed delay dependent stability conditions are established in terms of linear matrix inequalities based on this criterion a new sufficient delay and dependent condition is given to guarantee that the cnns with distributed delay are strictly q s dissipative the results developed in this brief can tolerate larger allowable delay than existing ones in the literature which is demonstrated by several examples

multiple fully adaptive notch filter design based on allpass sections we develop a canonical adaptive cascade structure iir notch filter to detect and track multiple time varying frequencies in additive white gaussian noise the algorithm uses allpass frequency transformation filters and a truncated gradient simulations indicate that our algorithm is computationally simple converges rapidly and has good frequency resolution

tracking analysis of a generalized adaptive notch filter the paper presents results of local performance analysis of a generalized adaptive notch filter ganf ganfs are used for identification tracking of quasi periodically varying dynamic systems and can be considered an extension to the system case of classical adaptive notch filters the tracking properties of the algorithm are studied analytically using a direct averaging approach and an approximating linear filter technique even though restricted to a single frequency case the presented analysis provides valuable insights into the tracking mechanisms of ganf including the associated speed accuracy tradeoffs the achievable performance bounds and tracking limitations in addition it allows one to formulate some useful rules of thumb for choosing design parameters

stable iir notch filter design with optimal pole placement this paper presents a two stage approach for designing an infinite impulse response iir notch filter first the numerator of the transfer function of the iir notch filter is obtained by placing the zeros at the prescribed notch frequencies then the denominator polynomial is determined by using an iterative scheme in which the optimal pole placements are found by solving a standard quadratic programming problem for stability the pole radius in the single notch filter design is specified by the designer and in the multiple notch filter design the pole radius is constrained by using the implications of rouche s theorem examples are included to illustrate the effectiveness of the proposed techniques