Delay‐dependent filtering design for time‐delay systems with Markovian jumping parameters

Two filtering problems—H_∞ filtering and H₂ filtering—for the linear Markovian jump systems with time delay are considered in this paper. The proposed new filtering approach guarantees that the results are less conservative than that obtained by other existing approaches. Numerical example well demonstrates the proposed algorithms. Copyright © 2006 John Wiley & Sons, Ltd.     

Constructing numerically stable Kalman filter‐based algorithms for gradient‐based adaptive filtering

This paper addresses the numerical aspects of adaptive filtering (AF) techniques for simultaneous state and parameters estimation arising in the design of dynamic positioning systems in many areas of research. The AF schemes consist of a recursive optimization procedure to identify the uncertain system parameters by minimizing an appropriate defined performance index and the application of the Kalman filter (KF) for dynamic positioning purpose. The use of gradient‐based optimization methods in the AF computational schemes yields to a set of the filter sensitivity equations and a set of matrix Riccati‐type sensitivity equations. The filter sensitivities evaluation is usually carried out by the conventional KF, which is known to be numerically unstable, and its derivatives with respect to unknown system parameters. Recently, a novel square‐root approach for the gradient‐based AF by the method of the maximum likelihood has been proposed. In this paper, we show that various square‐root AF schemes can be derived from only two main theoretical results. This elegant and simple computational technique replaces the standard methodology based on direct differentiation of the conventional KF equations (with their inherent numerical instability) by advanced square‐root filters (and its derivatives as well). As a result, it improves the robustness of the computations against round off errors and leads to accurate variants of the gradient‐based AFs. Additionally, such methods are ideal for simultaneous state estimation and parameter identification because all values are computed in parallel. The numerical experiments are given. Copyright © 2015 John Wiley & Sons, Ltd.     

State‐space equations of regular and strictly topologically degenerate linear lumped time‐invariant networks: the multiport method

A new complete approach to the multiport formulation of the state‐space equations of uniquely solvable regular or strictly topologically degenerate linear lumped time‐invariant networks is presented. It is based on a Gedankenexperiment during which the topological structure of the original network is manipulated in various ways. The final method requires one to calculate the describing matrices of three homogeneous multiports (i.e. a capacitive, an inductive and a resistive one), which are obtained from the network of interest in a very simple manner. As a by‐product, the equivalent partitioned network is also derived. As an example of application, the state‐space equations of a fourth‐order strictly topologically degenerate network are provided. Copyright © 2001 John Wiley & Sons. Ltd.     

Delay‐dependent robust stability analysis for interval neural networks with time‐varying delay

This article deals with the problem of robust stability for interval neural networks with time‐varying delay. By constructing an appropriate Lyapunov–Krasovskii functional, using the S‐procedure and taking the relationship among the time‐varying delay, its upper bound and their difference into account, some linear matrix inequality(LMI) ‐based delay‐dependent stability criteria are obtained without ignoring any terms in the derivative of the Lyapunov–Krasovskii functional. Finally, two numerical examples are given to demonstrate the effectiveness and benefits of the proposed method. © 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Observer design for neutral‐type neural networks with discrete and distributed time‐varying delays

This paper is concerned with the problem of state estimation for a class of neural networks with discrete and distributed interval time‐varying delays. We propose a new approach of nonlinear estimator design for the class of neutral‐type neural networks. By constructing a newly augmented Lyapunov‐Krasovskii functional, we establish sufficient conditions to guarantee the estimation error dynamics to be globally exponentially stable. The obtained results are formulated in terms of linear matrix inequalities (LMIs), which can be easily verified by the MATLAB LMI control toolbox. Then, the desired estimators gain matrix is characterized in terms of the solution to these LMIs. Three numerical examples are given to show the effectiveness of the proposed design method.     

On robust H∞ filtering of uncertain Markovian jump time‐delay systems

This paper is concerned with the problems of stability analysis, H_∞ performance analysis, and robust H_∞ filter design for uncertain Markovian jump linear systems with time‐varying delays. The purpose is to improve the existing results on these problems. Firstly, a new delay‐dependent stability criterion is obtained on the basis of a novel mode‐dependent Lyapunov functional. Secondly, a new delay‐dependent bounded real lemma (BRL) is derived. It is shown that the presented stability criterion and the BRL are less conservative than the existing ones in the literature. Thirdly, with the new BRL, delay‐dependent conditions for the solvability of the addressed H_∞ filtering problem are given. All the results obtained in this paper are expressed by means of strict linear matrix inequalities. Three numerical examples are provided to demonstrate the utility of the proposed methods. Copyright © 2011 John Wiley & Sons, Ltd.     

Delay‐dependent robust L2–L∞ filtering of T‐S fuzzy systems with time‐varying delays

This paper considers the problem of robust delay‐dependent L₂–L_∞ filtering for a class of Takagi–Sugeno fuzzy systems with time‐varying delays. The purpose is to design a fuzzy filter such that both the robust stability and a prescribed L₂–L_∞ performance level of the filtering error system are guaranteed. A delay‐dependent sufficient condition for the solvability of the problem is obtained and a linear matrix inequality (LMI) approach is developed. A desired filter can be constructed by solving a set of LMIs. Copyright © 2009 John Wiley & Sons, Ltd.     

Preliminary results from an analog implementation of first‐order TDCNN dynamics

This work falls into the category of linear cellular neural network (CNN) implementations. We detail the first investigative attempt on the CMOS analog VLSI implementation of a recently proposed network formalism, which introduces time‐derivative ‘diffusion’ between CNN cells for nonseparable spatiotemporal filtering applications—the temporal‐derivative CNNs (TDCNNs). The reported circuit consists of an array of Gm‐C filters arranged in a regular pattern across space. We show that the state–space coupling between the Gm‐C‐based array elements realizes stable and linear first‐order (temporal) TDCNN dynamics. The implementation is based on linearized operational transconductance amplifiers and Class‐AB current mirrors. Measured results from the investigative prototype chip that confirms the stability and linearity of the realized TDCNN are provided. The prototype chip has been built in the AMS 0.35 µm CMOS technology and occupies a total area of 12.6 mm sq, while consuming 1.2 µW per processing cell. Copyright © 2010 John Wiley & Sons, Ltd.     

H∞ filtering for continuous‐time singular systems with time‐varying delay

This paper focuses on H_∞ filter design for continuous‐time singular systems with time‐varying delay. A delay‐dependent H_∞ performance analysis result is first established for error systems via a novel estimation method. By combining a well‐known inequality with a delay partition technique, the upper bound of the derivative of the Lyapunov functional is estimated more tightly and expressed as a convex combination with respect to the reciprocal of the delay rather than the delay. Based on the derived H_∞ performance analysis results, a regular and impulse‐free H_∞ filter is designed in terms of linear matrix inequalities (LMIs). A numerical example is given to demonstrate the merits of the proposed method. Copyright © 2010 John Wiley & Sons, Ltd.     

Effects of leakage delay on global asymptotic stability of complex‐valued neural networks with interval time‐varying delays via new complex‐valued Jensen's inequality

This paper investigates the global asymptotic stability analysis for a class of complex‐valued neural networks with leakage delay and interval time‐varying delays. Different from previous literature, some sufficient information on a complex‐valued neuron activation function and interval time‐varying delays has been considered into the record. A suitable Lyapunov‐Krasovskii functional with some delay‐dependent terms is constructed. By applying modern integral inequalities, several sufficient conditions are obtained to guarantee the global asymptotic stability of the addressed system model. All the proposed criteria are formulated in the structure of a complex‐valued linear matrix inequalities technique, which can be checked effortlessly by applying the YALMIP toolbox in MATLAB linear matrix inequality. Finally, two numerical examples with simulation results have been provided to demonstrate the efficiency of the proposed method.     

Dissipativity and passivity analysis for discrete‐time complex‐valued neural networks with leakage delay and probabilistic time‐varying delays

In this paper, the problem of dissipativity and passivity analysis is investigated for discrete‐time complex‐valued neural networks with time‐varying delays. Both leakage and discrete time‐varying delays have been considered. By constructing a suitable Lyapunov–Krasovskii functional and by using discretized Jensen's inequality approach, sufficient conditions have been established to guarantee the (Q ,S ,R ) ? γ dissipativity and passivity of the addressed discrete‐time complex‐valued neural networks. These conditions are derived in terms of complex‐valued linear matrix inequalities (LMIs), which can be checked numerically using Yet Another LMI Parser toolbox in Matrix Laboratory. Finally, three numerical examples are established to illustrate the effectiveness of the obtained theoretical results. Copyright © 2016 John Wiley & Sons, Ltd.     

filtering for switched discrete‐time systems under asynchronous switching: A dwell‐time dependent Lyapunov functional method

In this article, the filtering problem for switched discrete‐time linear systems under asynchronous switching is addressed in the framework of dwell time, where ‘asynchronous switching’ covers more general and practical cases, for example, the switching lags caused by mode identification process are taken into consideration. Firstly, a novel dwell‐time dependent Lyapunov function (DTDLF) is introduced to solve stability and ?₂ gain analysis problems. The main advantage of DTDLF approach is that the derived conditions are all convex in system matrices, so it is convenient to be applied into filter design with performance instead of weighted performance as many other previous results. Thus, on the basis of DTLDF, a dwell‐time dependent filter with time‐varying structure is proposed to achieve the desirable non‐weighted filtering performance. It is notable that the proposed approach can also easily characterize the relationships among filtering performance, dwell time, and asynchronous time. Two examples are provided to validate the theoretical findings in this paper. Copyright © 2014 John Wiley & Sons, Ltd.     

A noise‐constrained algorithm for estimation over distributed networks

Much research has been devoted recently to the development of algorithms to utilize the distributed structure of an ad hoc wireless sensor network for the estimation of a certain parameter of interest. A successful solution is the algorithm called the diffusion least mean squares algorithm. The algorithm estimates the parameter of interest by employing cooperation between neighboring sensor nodes within the network. The present work derives a new algorithm by using the noise constraint that is based on and improves the diffusion least mean squares algorithm. In this work, first the derivation of the noise constraint‐based algorithm is given. Second, detailed convergence and steady‐state analyses are carried out, including analyses for the case where there is mismatch in the noise variance estimate. Finally, extensive simulations are carried out to test the robustness of the proposed algorithm under different scenarios, especially the mismatch scenario. Moreover, the simulation results are found to corroborate the theoretical results very well. Copyright © 2012 John Wiley & Sons, Ltd.     

Design considerations for low power time‐mode SAR ADC

This paper describes circuit design considerations for realization of low power dissipation successive approximation register (SAR) analog‐to‐digital converter (ADC) with a time‐mode comparator. A number of design issues related to time‐mode SAR ADC are discussed. Also, noise and offset models describing the impact of the noise and offset on the timing error of time‐domain comparator are presented. The results are verified by comparison to simulations. The design considerations mentioned in this paper are useful for the initial design and the improvements of time‐mode SAR ADC. Then, a number of practical design aspects are illustrated with discussion of an experimental 12‐bit SAR ADC that incorporates a highly dynamic voltage‐to‐time converter and a symmetrical input time‐to‐digital converter. Prototyped in a 0.18‐µm six‐metal one‐polysilicon Complementary Metal‐Oxide‐Semiconductor (CMOS) process, the ADC, at 12 bit, 500 kS/s, achieves a Nyquist signal‐to‐noise‐and‐distortion ratio of 53.24 dB (8.55 effective number of bits) and a spurious‐free dynamic range of 70.73 dB, while dissipating 27.17 μW from a 1.3‐V supply, giving a figure of merit of 145 fJ/conversion‐step. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust input‐output finite‐time filtering for uncertain Markovian jump nonlinear systems with partially known transition probabilities

In this paper, the robust input‐output finite‐time filtering problem is addressed for a class of uncertain Markovian jump nonlinear systems with partially known transition probabilities. Here, the disturbances, uncertainties, state delay, and distributed delays are all taken into account. Both the stochastic finite‐time boundedness and the stochastic input‐output finite‐time stability are introduced to the Markovian jump nonlinear systems with partially known transition probabilities. By constructing a reasonable stochastic Lyapunov functional and using linear matrix inequality techniques, sufficient conditions are established to guarantee the filtering error systems are stochastic finite‐time bounded and stochastic input‐output finite‐time stable, respectively. Finally, 2 examples are provided to illustrate the effectiveness of the proposed methods.     

Convergence of discrete‐time cellular neural networks with interlaced block‐sequential dynamics

Some interlaced block‐sequential modes of operation are introduced for discrete‐time cellular neural networks (DTCNN), and the corresponding convergence conditions are investigated. It is proved that DTCNNs, under some block‐sequential updating rules, result to be convergent when the feedback templates satisfy some restrictions rather milder than reciprocity or dominance, as required in synchronous mode. Moreover, the set of fixed points of the network results to be independent of the particular updating rule adopted. The drawback of desynchronization is a reduced speed of convergence, which however is tolerable in the usual case when the neighbourhood radius is small. Copyright © 1999 John Wiley & Sons, Ltd.     

A controllable multi‐replica routing approach for opportunistic networks

Opportunistic networks take full advantage of opportunistic encounters among nodes to transfer packets. According to the characteristics of the limited energy of nodes and the frequent link variation in opportunistic networks, we introduce a novel routing metric that comprehensively takes into consideration the energy consumption of nodes, the probability of relay nodes meeting their destination, the time‐to‐live of the packet, and the approximate number of packet copies. Based on this metric, we propose a controllable multi‐replica routing approach in which a single‐branch diffusion strategy is applied to achieve the goal of dynamically controlling the number of replicas of packets. Our simulation results show that the proposed approach can avoid excessive load on individual nodes, guarantee the energy fairness among nodes, prolong the network lifetime, and effectively improve the delivery ratio of packets. © 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

Delay‐dependent filtering for discrete‐time state‐delayed systems with small gain conditions in finite frequency ranges

This paper is concerned with the delay‐dependent filtering problem for linear discrete‐time multi‐delay systems with small gain conditions in finite frequency ranges. A new multiplier method is developed to convert the resulting nonconvex filtering synthesis conditions to the ones based on linear matrix inequalities (LMIs). Thus, sufficient conditions for the existence of feasible filters are given in terms of solutions to a set of LMIs. For the entire frequency case, it is shown that the proposed result is less conservative than the relative existing results. Finally, the procedures and the advantages of the proposed approach in comparison with the existing ones in the entire frequency range are illustrated via numerical examples. Copyright © 2011 John Wiley & Sons, Ltd.     

Bottom‐up performance analysis of focal‐plane mixed‐signal hardware for Viola–Jones early vision tasks

Focal‐plane mixed‐signal arrays have traditionally been designed according to the general claim that moderate accuracy in processing is affordable. The performance of their circuitry has been analyzed in these terms without a comprehensive study of the ultimate consequences of such moderate accuracy. In this paper, for the first time to the best of our knowledge, we do carry out this study. We move expectable performance of mixed‐signal image processing hardware directly into the vision algorithm making use of it. This permits to close a wider design loop, enabling a more aggressive design of this kind of hardware provided that the algorithm, at the highest level—semantic interpretation of the scene—, can afford it. Thus, we present a thorough analysis of the non‐idealities associated with the implementation of a QVGA array tailored for the distinctive characteristics of the Viola–Jones processing framework. The resulting deviation models are then introduced in the processing flow of this framework provided by the OpenCV library. We have found, contrary to what could be expected, that these deviations do not necessarily degrade the performance of the Viola–Jones algorithm. They could be even beneficial for certain high‐level specifications. Additionally, we demonstrate the architectural advantages of our approach: exploitation of focal‐plane distributed memory and ultra‐low‐power operation. Copyright © 2014 John Wiley & Sons, Ltd.     

Implementation of a square‐root filtering approach in marginalized particle filters for mixed linear/nonlinear state‐space models

Marginalized particle filter (MPF) takes advantage of both Kalman filter and particle filter frameworks to estimate nonlinear state‐space models with reduced number of calculations in comparison to particle filter. However, due to existence of Kalman filter framework inside MPF, some limitations are introduced in implementation of MPF especially in embedded systems with finite numerical accuracies. In this paper, for the first time, we propose a novel square‐root filtering strategy for MPFs to alleviate these restrictions using modified factorization. Typical square‐root Kalman filters cannot be employed inside MPF due to the presence of minus operations in some equations of MPF. However, our method can be easily implemented inside the MPF structure. The proposed method can be used in any application that employs MPFs to estimate the mixed linear/nonlinear state‐space models. In order to demonstrate its usefulness, we employed the proposed square‐root filtering method inside a marginalized particle extended Kalman filter (MP‐EKF) structure, which was specifically designed for ECG denoising. The experimental results showed that, in the field of ECG denoising, the square‐root MP‐EKF performs more consistently than MP‐EKF in white Gaussian noises.