External inputs,stable equilibria and complete stability of CNNs

We construct two cellular neural networks (CNNs) of three cells to show that a CNN can have stable equilibria, but is not completely stable and that the complete stability of CNN depends on the choice of external inputs. These phenomena cannot occur for two‐cell CNNs. Copyright © 2003 John Wiley & Sons, Ltd.     

Łojasiewicz inequality and exponential convergence of the full‐range model of CNNs

This paper considers the Full‐range (FR) model of Cellular Neural Networks (CNNs) in the case where the signal range is delimited by an ideal hard‐limiter nonlinearity with two vertical segments in the i?v characteristic. A ?ojasiewicz inequality around any equilibrium point, for a FRCNN with a symmetric interconnection matrix, is proved. It is also shown that the ?ojasiewicz exponent is equal to . The main consequence is that any forward solution of a symmetric FRCNN has finite length and is exponentially convergent toward an equilibrium point, even in degenerate situations where the FRCNN possesses non‐isolated equilibrium points. The obtained results are shown to improve the previous results in literature on convergence or almost convergence of symmetric FRCNNs. Copyright © 2010 John Wiley & Sons, Ltd.     

On complete stability of linear and quadratic programming neural networks

The paper addresses complete stability (CS) of the important class of neural networks to solve linear and quadratic programming problems introduced by Kennedy and Chua (IEEE Trans. Circuits Syst., 1988; 35 : 554). By CS it is meant that each trajectory converges to a stationary state, i.e. an equilibrium point of the neural network. It is shown that the neural networks in (IEEE Trans. Circuits Syst., 1988; 35 : 554) enjoy the property of CS even in the most general case where there are infinite non‐isolated equilibrium points. This result, which is proved by exploiting a new method to analyse CS (Int. J. Bifurcation Chaos 2001; 11 : 655), extends the stability analysis by Kennedy and Chua (IEEE Trans. Circuits Syst., 1988; 35 : 554) to situations of interest where the optimization problems have infinite solutions. Copyright © 2002 John Wiley & Sons, Ltd.     

Power packet dispatching with second‐order clock synchronization

Aiming to distinguish sources and loads for the rapid scaling of renewable power sources, a novel power packet dispatching system has been proposed with a structure similar to those of communication systems. Accurate clock synchronization is necessary to achieve the exchange of information attached in a power packet. At the same time, the clock synchronization is an interesting topic for developing power packet dispatching between multi‐sender and a receiver. This work outlines the implementation of second‐order autonomously controlled clock synchronization into the power packet dispatching system. At first, we derive the second‐order controlled model of a digital clock synchronization method and analyze the stability of the developed model. Secondly, through experiments, it is shown that the information of power packets can be recognized correctly and the power can be distributed to directed loads. This paper demonstrates the validity of the asynchronous power packet dispatching based on our derived model. Copyright © 2015 John Wiley & Sons, Ltd.     

Adaptive output‐feedback stabilisation of an uncertain second‐order linear systems

The adaptive stabilisation of uncertain second‐order linear systems is addressed, under a lack of information from both states and parameters. The only standard assumptions are no zeros and the sign of the high frequency gain known. To the best of the authors' knowledge, there has been only an explicit solution proposed in the literature so far with proven stability. Despite the simplicity of the system, it does not fit in any of the standard nonlinear control methodologies available. Thus, this work is a complementary contribution providing a mixed control design strategy based on a reduced‐order observer, adaptive Immersion & Invariance and Backstepping approaches. Hence, this solution depicts a transversal outlook of those nonlinear control strategies and provides a breakthrough for the generalisation of this non‐trivial control problem. Numerical simulations are reported to assess the effectiveness of the adaptive strategy. Copyright © 2016 John Wiley & Sons, Ltd.     

Current‐input current‐output analog half center oscillator and central pattern generator circuits with memristors

The comparison of the memristor to the biological synapse has motivated the introduction of memristors to biomimetic circuits such as Central Pattern Generators (CPGs) and Half Center Oscillators (HCOs). The effects of the utilization of memristors in such systems have been investigated in this work. The HCO is a neural oscillator, and the CPG is made up of 4 HCOs producing oscillations corresponding to the locomotion of a 4‐limbed animal. Analog HCO and CPG circuits have been simulated using the Cadence Virtuoso platform and effects of using current‐driven and voltage‐driven memristors in different configurations with different parameters have been analyzed. Improvement in the stability of rhythm and variations in oscillation amplitudes have been observed.     

Global exponential stability of a class of impulsive cellular neural networks with supremums

In this paper, we study the problem of global exponential stability for impulsive cellular neural networks with time‐varying delays and supremums. Using Young's inequality and Lyapunov‐like functions, new stability criteria are proved. Because supremums and impulses are relevant in various contexts, including problems in the theory of automatic control, our results can be applied in the qualitative investigations of many practical problems of diverse interest. Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

On dissipative two‐state quantum cells and cellular networks

Quantum cellular networks are structures composed of two‐state quantum cells in equilibrium with a stationary thermal bath, which interact among themselves in a classical way. It is shown that if system–bath coupling is set up in the representation based on the given states, as done in the literature, or in the energy representation, as the theory assumes under several approximations, different models are obtained for the cell performance, and hence for the dynamics of the whole network. The model based on the state representation could also be derived by a heuristic approach. The final choice should be decided by experiment. Copyright © 2004 John Wiley & Sons, Ltd.     

Current‐mode programmable piecewise‐linear neural synapses

Cellular Neural Networks with piecewise linear connection have been proposed by several authors as a generalization of the basic paradigm, which allows for more complex functionality. None of the prototypes realized to date, however, provides for such kind of synapses. As a feasibility study, a current‐mode subthreshold CMOS piecewise‐linear synapse circuit is developed in this paper. Copyright © 2003 John Wiley & Sons, Ltd.     

A novel low‐spur DDFS utilizing the twice second‐order high‐pass error feedback architecture

In this paper, the twice second‐order high‐pass error feedback (EF) (twice second‐order high‐pass EF (HPEF)) utilizing re‐feedback process and phase‐bit splitting technique in the second‐order HPEF to design a simplified low‐spur direct digital frequency synthesizer is proposed. The proposed method performs phase‐bit splitting technique and re‐feedback process in order to make the phase change tremendously and scramble the periodicity of the phase sequences violently in the original feedback path. In addition, the noise spectrum power is spread more uniformly in order to effectively suppress the spurs due to phase‐truncated error effect. Thus, the twice second‐order HPEF is implemented on a field programmable gate array development board, the Altera Stratix II EP2S60. The simulation and experimental results show that the proposed method can effectively achieve better spectrum performance, such as spurious‐free dynamic range, as compared to basic phase truncation, first‐order HPEF and second‐order HPEF architectures. Copyright © 2012 John Wiley & Sons, Ltd.     

非对称细胞神经网络稳定平衡点的存在性

利用饱和域的特性,和拟对角列支配矩阵与M-矩阵之间的关系,获得了非对称细胞神经网络(简称CNNS)的稳定平衡点存在的两个充分条件;在此基础上,通过定义一个更高阶的神经网络模型,推广了该网络存在稳定平衡点的结果;通过大量的模拟仿真,提出了非对称细胞神经网络完全稳定的充分条件,并就二细胞神经网络的情况给予了证明;最后,将本文的结果与已取得的结果进行了比较,并给出一个实例说明本文取得的结果优于已有文献取得的结果.     

Signal separation using second‐ and high‐order statistics

This paper presents two methods for signal separation. In either method, the fundamental criterion for separation relies on reducing to zero, or at least minimizing, the output cross‐correlation or cross‐cumulant functions of a decoupling multi‐input–multi‐output system that is fed with mixed signals. In one of the approaches used, the parameters of this system are determined through solving — in a least‐squares sense — a linearized set of equations describing the deviations from zero of either the cross‐correlation or cross‐cumulant functions when evaluated for different lags. An alternative rapidly convergent adaptive algorithm is also described for minimizing the cross‐correlation or cross‐cumulant functions. The paper also considers both FIR and IIR representations of the decoupling system. It shows that using IIR functions in the decoupling system does not offer any merit over the FIR case. Illustrative examples are given to show the performance of the proposed algorithms. Copyright © 2000 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.     

Pixel‐level snakes on the CNNUM: algorithm design,on‐chip implementation and applications

In this paper, a new algorithm for the cellular active contour technique called pixel‐level snakes is proposed. The motivation is twofold: on the one hand, a higher efficiency and flexibility in the contour evolution towards the boundaries of interest are pursued. On the other hand, a higher performance and suitability for its hardware implementation onto a cellular neural network (CNN) chip‐set architecture are also required. Based on the analysis of previous schemes the contour evolution is improved and a new approach to manage the topological transformations is incorporated. Furthermore, new capabilities in the contour guiding are introduced by the incorporation of inflating/deflating terms based on the balloon forces for the parametric active contours. The entire algorithm has been implemented on a CNN universal machine (CNNUM) chip set architecture for which the results of the time performance measurements are also given. To illustrate the validity and efficiency of the new scheme several examples are discussed including real applications from medical imaging. Copyright © 2005 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.     

A low‐voltage band‐gap reference circuit with second‐order analyses

A new band‐gap reference (BGR) circuit employing sub‐threshold current is proposed for low‐voltage operations. By employing the fraction of V_BE and the sub‐threshold current source, the proposed BGR circuit with chip area of 0.029mm² was fabricated in the standard 0.18µm CMOS triple‐well technology. It generates reference voltage of 170 mV with power consumption of 2.4µW at supply voltage of 1 V. The agreement between simulation and measurement shows that the variations of reference voltage are 1.3 mV for temperatures from ?20 to 100^°C, and 1.1 mV per volt for supply voltage from 0.95 to 2.5 V, respectively. Copyright © 2010 John Wiley & Sons, Ltd.     

Absolute stability of a class of neural networks with unbounded delay

In this paper, the existence and uniqueness of the equilibrium point and absolute stability of a class of neural networks with partially Lipschitz continue activation functions are investigated. The neural networks contain both variable and unbounded delays. Using the matrix property, the necessary and sufficient condition for the existence and uniqueness of the equilibrium point of the neural networks is obtained. By constructing proper vector Liapunov functions and non‐linear integro‐differential inequalities involving both variable delays and unbounded delay, the sufficient conditions for absolute stability (global asymptotic stability) are obtained. Copyright © 2004 John Wiley & Sons, Ltd.     

Stability analysis and robust synchronization of fractional‐order competitive neural networks with different time scales and impulsive perturbations

This article mainly examine a class of robust synchronization, global stability criterion, and boundedness analysis for delayed fractional‐order competitive type‐neural networks with impulsive effects and different time scales. Firstly, by endowing the robust analysis skills and a new class of Lyapunov‐Krasovskii functional approach, the error dynamical system is furnished to be a robust adaptive synchronization in the voice of linear matrix inequality (LMI) technique. Secondly, by ignoring the uncertain parameter terms, the existence of equilibrium points are established by means of topological degree properties, and the solution representation of the considered network model are provided. Thirdly, a novel global asymptotic stability condition is proposed in the voice of LMIs, which is less conservative. Finally, our analytical results are justified with two numerical examples with simulations.