Complete stability of feedback CNNs with dynamic memristors and second‐order cells

The paper considers a feedback cellular neural network (CNN) obtained by interconnecting elementary cells with an ideal capacitor and an ideal flux‐controlled memristor. It is supposed that during the analogue computation of the CNN the memristors behave as dynamic elements, so that each dynamic memristor (DM)‐CNN cell is described by a second‐order differential system in the state variables given by the capacitor voltage and the memristor flux. The proposed networks are called DM‐CNNs, that is CNNs using a dynamic (D) memristor (M). After giving a foundation to the DM‐CNN model, the paper establishes a fundamental result on complete stability, that is convergence of solutions toward equilibrium points, when the DM‐CNN has symmetric interconnections. Because of the presence of dynamic memristors, a DM‐CNN displays peculiar and basically different dynamic properties with respect to standard CNNs. First of all a DM‐CNN computes during the time evolution of the memristor fluxes, instead of the capacitor voltages as for a standard CNN. Furthermore, when a steady state is reached, the memristors keep in memory the result of the computation, that is the limiting values of the fluxes, while all memristor currents and voltages, as well as all currents, voltages, and power in the DM‐CNN vanish. Instead, for standard CNNs, currents, voltages, and power do not drop off when a steady state is reached. Copyright © 2016 John Wiley & Sons, Ltd.     

Single‐layer perceptron and dynamic neuron implementing linearly non‐separable Boolean functions

This paper presents a single‐layer perceptron (SLP) scheme with an impulse activation function (IAF) and a dynamic neuron (DN) with a trapezoidal activation function (TAF). Combining with some interesting properties of the offset levels, it is shown that many linearly non‐separable Boolean functions can be realized by using only one SLPwIAF or one DNwTAF. In the present work, a few appropriate IAF and TAF are adopted, and the inverse offset level method is used for the design of the SLPwIAF synaptic weights and the DNwTAF templates. The XOR and NXOR Boolean operations with two inputs and all 152 non‐separable Boolean functions with three inputs can be easily implemented by one SLPwIAF or one DNwTAF. Finally, the entire set of 152 DNwTAF templates associated with 152 non‐separable Boolean functions of three inputs is completely listed. Copyright © 2008 John Wiley & Sons, Ltd.     

Cellular neural networks based on resonant tunnelling diodes

Resonant tunnelling diodes (RTDs) have intriguing properties which make them a primary nanoelectronic device for both analogue and digital applications. We propose two different types of RTD‐based cells for the cellular neural network (CNN) which exhibit superior performance in terms of complexity, functionality, or processing speed compared to standard cells. In the first cell model, the resistor of the standard cell is replaced by an RTD, which results in a more compact and versatile cell which requires neither self‐feedback nor a non‐linear output function, and allows three stable equilibrium points. If a resonant tunnelling transistor (RTT) is used instead of the RTD, the dynamics can be controlled through its gate voltage as an additional network parameter. In a majority of CNN applications, bistable cells are sufficient. Utilizing RTD‐based bistable logic elements to store the state of the cell, switching occurs almost instantaneously as virtually no charge transfer is necessary, and it is possible to implement non‐linear connections in a straightforward manner. Hence, it turns out that RTD‐based CNNs are tailor‐made for the implementation of extremely fast bipolar operations and non‐linear templates. The ideas presented in this paper may also be beneficially applied to other types of circuits and systems such as A/D converters or sigma‐delta modulators. Copyright © 2001 John Wiley & Sons, Ltd.     

Semi‐adaptive control of convexly parametrized systems with application to temperature regulation of chemical reactors

In this paper, we are interested in the problem of adaptive control of non‐linearly parametrized systems. We investigate the viability of defining a stabilizing parameter update law for the case when the plant model is convex on the uncertain parameters. We show that, when the only prior knowledge is convexity, there does not exist an adaptation law—derivable from the standard separable Lyapunov function technique of Parks—applicable for all the state space. Therefore, we propose a semi‐adaptive state feedback controller where adaptation takes place only in the region of the state space where convexity can be used to reduce parameter uncertainty. In the remaining part of the state space we freeze the adaptation and switch to a robust controller. This scheme ensures semi‐global stability for convexly parametrized non‐linear systems with matched uncertainty. The proposed controller is then applied to the problem of temperature regulation of continuous stirred exothermic chemical reactors where reaction heat is convex in the uncertain parameters. Copyright © 2001 John Wiley & Sons, Ltd.     

Cellular neural networks with opposite‐sign templates for image thinning

This paper presents image thinning algorithms using cellular neural networks (CNNs) with one‐ or two‐dimensional opposite‐sign templates (OSTs) as well as non‐unity gain output functions. Two four‐layer CNN systems with one‐dimensional (1‐D) OSTs are proposed for image thinning with 4‐ or 8‐connectivity, respectively. A CNN system, which consists of an eight‐layer CNN with two‐dimensional (2‐D) OSTs followed by another four‐layer CNN with 2‐D OSTs, is constructed for image thinning with 8‐connectivity, in which designs of B‐ and I‐templates are simpler than in CNNs with 1‐D OSTs. In the aforementioned designs, parameter values of 1‐D OSTs are chosen to make CNNs operate with thinning‐like property 1 (TL‐1), and those of 2‐D OSTs with 2‐D thinning‐like property (2‐DTL). Simulation studies show that these CNN systems have a good image thinning performance. Copyright © 1999 John Wiley & Sons, Ltd.     

The analogic cellular neural network as a bionic eye

The conception of the CNN universal machine has led quite naturally to the invention of the analogic CNN bionic eye (henceforth referred to simply as the bionic eye). the basic idea is to combine the elementary functions, the building blocks, of the retina and other 2 1/2 D sensory organs, algorithmically, in a stored programme of a CNN universal machine, through the use of artificial analogic programmes. the term bionic is defined in a rigorous way: it is a nonlinear, dynamic, spatiotemporal biological model implemented in a stored programme electronic (optoelectronic) device; this device is in our case the analogic CNN universal machine (or chip). The aim of this paper is to report on this new invention, particularly to electronic and computer engineers, in a tutorial way. We begin by summarizing (1) the biological aspects of the range of retinal function (the retinal universe), (2) the CNN paradigm and the CNN universal machine architecture and (3) the general principles of retinal modelling in CNN. Next we describe new CNN circuit and template design innovations that can be used to implement physiological functions in the retina and other sensory organs using the CNN universal machine. Finally we show how to combine given retinal functional elements implemented in the CNN universal machine with analogic algorithms to form the bionic retina. the resulting system can be used not only for simulating biological retinal function but also for generating functions that go far beyond biological capabilities. Several bionic retina functions, different topographic modalities and analogic CNN algorithms can then be combined to form the analogic CNN bionic eye. the qualitative aspects of the models, especially the range of dynamics and accuracy considerations in VLSI optoelectronic implementations, are outlined. Finally, application areas of the bionic eye and possibilities of constructing innovative devices based on this invention (such as the bionic eyeglass or the visual mouse) are described.     

Accelerating the transient simulation of semiconductor devices using filter‐bank transforms

Simulation of high frequency semiconductor devices, where non‐local and hot carrier transport cannot be ignored, requires solution of Poisson's equation and at least the first three moments of the Boltzmann transport equation (hydrodynamic transport model). These equations form non‐linear, coupled and time‐dependent partial differential equations. One of the most efficient solvers of such system of equations is decoupled solver. In conventional decoupled methods, the fully implicit, semi‐implicit and explicit methods are used to solve the equations. In fully or semi‐implicit schemes, the method is unconditionally stable for any Δt or for very large Δt compared to explicit scheme. Thus, these schemes are very suitable and efficient for transient simulations. But, using these techniques leads to a large system of linear equations. Here for the first time, a filter bank‐based preconditioning method is used to facilitate the iterative solution of this system. This method provides efficient preconditioners for matrices arising from discretizing of the PDEs, using finite difference techniques. Numerical results show that the condition number and iteration number are significantly reduced. The most important advantage of this preconditioner is its low computational complexity which can be reduced to O(N). Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive state feedback control by orthogonal approximation functions

For a class of feedback linearizable systems a state feedback adaptive control based on orthogonal approximation functions is designed, under the assumption of matching conditions for the uncertainties and of known bounds on a given compact set for the unknown non‐linear function. By virtue of Bessel inequality, the bound on the unknown non‐linear function gives a bound on the norm of the optimal weight vector for any choice of the number of approximating functions, which allows us to design a robust state feedback adaptive scheme with parameter projections. The resulting control algorithm has several advantages over available schemes: it does not require a priori bounds on the approximation error and on the optimal weight vector; it is repeatable, since the set of initial conditions for the state and the parameter estimates from which a class of reference signals is tracked is explicitly given; it characterizes the L_∞ and L₂ performance of the tracking error in terms of both the approximation and the parameter estimation error; it gives full flexibility in the choice of the number of approximating orthogonal functions. Copyright © 2002 John Wiley & Sons, Ltd.     

Two‐dimensional non‐stationary numerical model for MSM structures

In this paper the solution of the non‐stationary model for MSM structures is obtained numerically. The two‐dimensional model consists of three singularly perturbed non‐linear partial differential equations. The alternating‐direction method for discretization in time and the non‐oscillatory streamline upwind method on a piecewise uniform grid for discretization in space are used to eliminate the interior and boundary layer oscillations. The described model is used for the analysis of the time response of a GaAs n‐type MSM structure to a Heaviside function form of the applied voltage. For the stationary case the I–V characteristic of the structure is determined. The numerical results confirm that the applied method is convenient for solving convection–diffusion problems. Copyright © 1999 John Wiley & Sons, Ltd.     

Sliding mode control of singularly perturbed Markovian jump delayed systems with nonlinearity

This paper is concerned with sliding mode control (SMC) of a class of time‐delay nonlinear singularly perturbed Markovian jump systems. Firstly, a switching surface function is designed, and a delay‐dependent condition is derived in terms of ?‐independent linear matrix inequality, which guarantees that the resulting sliding mode dynamics is mean‐square exponentially stable. Then an algorithm is given to estimate the stability bound. Moreover, an adaptive SMC law is synthesized to drive the system trajectories onto the designed switching surface in a finite time. Finally, a numerical example is presented to show the effectiveness of proposed method. Copyright © 2012 John Wiley & Sons, Ltd.     

DC analysis and design of a PWM buck converter operated as a dynamic power supply

Modern energy transmission and signal reproduction techniques rely upon power amplifiers that must operate with high efficiency. An increasingly popular technique for addressing this problem involves replacing the fixed power amplifier supply voltage V _{D D} with a controlled, variable voltage provided by a dynamic power supply. Although pulse‐width modulated dc‐dc buck converters typically function as fixed‐output supplies, this paper provides new theoretical dc analysis for operation wherein the output voltage is controlled and continuously variable over a wide range. A design procedure for the variable‐output buck converter is derived. Key device parameters affecting converter speed and efficiency are identified. The dc analysis and design procedure are verified experimentally, with calculated and measured parameters shown to be in good agreement. Copyright © 2016 John Wiley & Sons, Ltd.     

可调速双馈水轮发电机组控制系统的稳定性分析

为了研究可调速双馈水轮发电系统静态稳定性和控制器反馈系数之间的关系，该文针对变速恒频双馈发电机运行特点和水轮机导叶开度调节结构，首先推导了具有刚性水锤效应的可调速双馈水轮发电机组的数学模型。其次，结合基于动态同步轴系下的双馈发电机双通道励磁控制策略以及有功功率参与反馈控制的水轮机调节系统模型，建立了以有功、无功和转差率为系统调节变量的闭环控制系统的状态方程。最后，利用李雅普诺夫函数概念和方法得到了闭环控制系统具有大域渐近稳定性的充分条件。分析结果表明，可调速双馈水轮发电机组的静态稳定性与控制器的反馈系数密切相关。     

A cellular non‐linear network for image fusion based on data regularization

In this paper, a synthesis method developed in the last few years is applied to derive a cellular non‐linear network (CNN) able to find an approximate solution to a variational image‐fusion problem. The functional to be minimized is based on regularization theory and takes into account two complementary principles, namely, knowledge source corroboration and belief enhancement/withdrawal, both typical of data‐fusion approaches. The obtained CNN has been tested by simulations (i.e. by numerically integrating the circuit state equations) in some case studies. The quality of the results is good, as turns out from comparisons with some standard methods. Copyright © 2006 John Wiley & Sons, Ltd.     

Adaptive control of non‐linear systems with unknown virtual control coefficients

This paper deals with state feedback adaptive control of parametric‐strict‐feedback (triangular) non‐linear systems with unknown virtual control coefficients. A priori knowledge of the signs of the virtual coefficients is not required, and control signals and adaptive laws are smooth. Asymptotic tracking of smooth reference signals is achieved while all the variables remain bounded. The proposed algorithms make use of backstepping and tuning functions, and enlarge the class of non‐linear systems with unknown parameters for which asymptotic output tracking can be achieved. Copyright © 2000 John Wiley & Sons, Ltd.     

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.     

Design methodology for common‐mode stability of OTA‐based gyrators

This paper presents a design methodology for common‐mode (CM) stability of operational transconductance amplifier (OTA)‐based gyrators. The topology of g_m ? C active inductors is briefly reviewed. Subsequently, a comprehensive mathematical analysis on the CM stability of OTA‐based gyrators is presented. Sufficient requirements for the gyrator's CM stability, which easily can be considered during the design process of common‐mode feedback (CMFB) amplifiers, are defined. Based on these stability requirements, a design methodology and a design procedure are proposed. Finally, in order to validate the proposed procedure, a resonator with 20 MHz resonance frequency and a quality factor of 20 is fabricated with UMC 180 nm complementary metal‐oxide‐semiconductor technology, and its CM stability is examined. Copyright © 2015 John Wiley & Sons, Ltd.     

A new algorithm for shot noise removal in medical ultrasound images based on alpha‐stable model

Ultrasonic images are generally affected by multiplicative shot noise. Shot noise filtering is thus a critical pre‐processing step in medical ultrasound imagery. This paper analyses and models the coefficients of 2‐D multi‐resolution wavelet decomposition of logarithmically transformed images using alpha‐stable distribution model. Consequently, we propose a new function that performs a non‐linear operation on the data of classifying the coefficients, thus achieving a novel form of noise removal based on multi‐resolution wavelet decomposition and the alpha‐stable model. We compare our new technique with current shot noise reduction methods applied on actual ultrasound medical images and simulations results show that the proposed new method is more robust than the methods based on Gaussian assumption. Copyright © 2006 John Wiley & Sons, Ltd.     

Linear repetitive learning controls for nonlinear systems by Padé approximants

We discuss the use of [m,m]‐Padé approximants in the implementation of repetitive learning controls solving the output tracking problem (via output error feedback) in the presence of uncertain periodic reference and/or disturbance signals with known common period. The aim is to address the stability issues concerning those approximants when a linear learning controller—designed through a detailed stability proof (involving the use of a suitable Lyapunov‐like function) and described by a transfer function exhibiting all its poles with negative real part—is to be obtained as well as to evaluate the corresponding closed‐loop performances: robustness (for instance with respect to additive disturbance noises due to unmodeled sensor dynamics) is consequently achieved with improvements in the output tracking errors appearing as the approximation order m increases. Even though the case of any relative degree may be explicitly addressed, in this paper, for the sake of clarity, we restrict our attention to the learning problem for the class of single‐input, single‐output, minimum phase, time‐invariant systems with known relative degree ρ = 2, uncertain parameters and uncertain output‐dependent nonlinearities. Numerical simulation results illustrate the theoretical derivations. Copyright © 2014 John Wiley & Sons, Ltd.