Electromagnetic non‐destructive evaluation by vector finite‐element neural networks

This study proposes neural network‐based iterative inverse solutions for non‐destructive evaluation (NDE) in which vector finite elements (VFEM) represent the forward model that closely models the physical process. The iterative algorithm can eventually estimate the material parameters. Vector finite element method global matrix is stored in a compact form using its sparsity and symmetry. The stored matrix elements are employed as the neurons weights, and preconditioning techniques are used to accelerate convergence of the neural networks (NN) algorithm. Detailed algorithm describing this new method is given to facilitate implementation. Combining vector finite elements and NNs offers several advantages over each technique alone, such as reducing memory storage requirements and the easily computed fixed weights of the NN. Various examples are solved to show the performance and usefulness of the proposed method, including lossy printed circuit board and lossy inhomogeneous cylindrical problems with ferromagnetic materials. These solutions compare very well with other published data where the maximum relative error was 5%. Copyright © 2010 John Wiley & Sons, Ltd.     

Synthesis of n‐port resistive networks containing 2n terminals

This paper is concerned with the realizability problem of n‐port resistive networks that contain 2n terminals. A necessary and sufficient condition for any real symmetric matrix to be realizable as the admittance of an n‐port resistive network containing 2n terminals is obtained. This condition is based on the existence of a parameter matrix. Furthermore, the values of the elements are expressed in terms of the entries of the admittance matrix and the parameter matrix. Finally, a numerical example is used to illustrate the results. Copyright © 2013 John Wiley & Sons, Ltd.     

Symmetrical two‐port networks: An eigenspace perspective

In this letter, it is shown how the canonical circuits for the synthesis of symmetrical two‐port networks can be derived from the eigendecomposition of the impedance matrix. Extensions to reciprocal two‐port networks are discussed. Copyright © 2010 John Wiley & Sons, Ltd.     

Time domain and steady‐state sensitivity of periodic switched networks to element changes

Switched periodic networks are widely used in power engineering and sensitivities of their steady‐state to element changes are of interest to the designers. The paper develops a method to obtain steady‐state sensitivities for networks in which semiconductor and other switching devices are modelled as ideal short circuits without additional elements. Such modelling has the advantage that switching transients need not be calculated and correct initial conditions after switching are obtained with only four integration steps. It is also shown that sensitivities need not be calculated during the process of reaching the steady‐state. Once the steady‐state is available, integration over two additional periods is sufficient to provide time domain steady state sensitivity to the element changes. Copyright © 1999 John Wiley & Sons, Ltd.     

Electrical characterization of linear and non‐linear random networks and mixtures

A material composed of a mixture of distinct homogeneous media can be considered as a homogeneous one at a sufficiently large observation scale. The problem of mixture characterization has been exactly solved in case of linear random mixtures, that is, materials for which the various components are isotropic, linear and mixed together as an ensemble of particles having random shapes and positions. In the present work the authors briefly review the linear theory and then consider mixtures of non‐linear media. In particular they give formulas for obtaining their constitutive equations for current density, electrical displacement, and magnetic induction. These relations have been derived by means of heuristic considerations on random networks and they have been verified with simulations obtaining a high degree of accuracy. Copyright © 2003 John Wiley & Sons, Ltd.     

All‐MOS implementation of RC networks for time‐controlled Gaussian spatial filtering

This paper addresses the design and VLSI implementation of MOS‐based RC networks capable of performing time‐controlled Gaussian filtering. In these networks, all the resistors are substituted one by one by a single MOS transistor biased in the ohmic region. The design of this elementary transistor is carefully realized according to the value of the ideal resistor to be emulated. For a prescribed signal range, the MOSFET in triode region delivers an interval of instantaneous resistance values. We demonstrate that, for the elementary 2‐node network, establishing the design equation at a particular point within this interval guarantees minimum error. This equation is then corroborated for networks of arbitrary size by analyzing them from a stochastic point of view. Following the design methodology proposed, the error committed by an MOS‐based grid when compared with its equivalent ideal RC network is, despite the intrinsic nonlinearities of the transistors, below 1% even under mismatch conditions of 10%. In terms of image processing, this error hardly affects the outcome, which is perceptually equivalent to that of the ideal network. These results, extracted from simulation, are verified in a prototype vision chip with QCIF resolution manufactured in the AMS 0.35µm CMOS‐OPTO process. This prototype incorporates a focal‐plane MOS‐based RC network that performs fully programmable Gaussian filtering. Copyright © 2011 John Wiley & Sons, Ltd.     

Realization of a transfer function as a passive two‐port RC ladder network with a specified gain

This paper is concerned with the realization problem for a class of high‐pass as well as low‐pass transfer functions as a two‐port RC ladder network with a specified gain, which requires that the actual gain of the realization configuration is equal to the gain of the given transfer function. From a cascade realization approach, it is shown that any transfer function belonging to the class under investigation can be realized as a two‐port RC ladder network with any specified gain in a continuous interval. Finally, a numerical example is presented to illustrate the result. Copyright © 2017 John Wiley & Sons, Ltd.     

Cellular non‐linear networks for minimization of functionals. Part 2: Examples

A method for the definition of cellular non‐linear networks able to find approximate minima of rather a large class of continuous functionals is illustrated through three examples. The method, based on the spatial discretization of continuous functionals and on the theory of potential functions for resistive circuits, has been presented in Part 1 of this paper. The first example (related to electromagnetic‐field theory) has the main purpose to show some aspects of the application procedure. The other two examples concern, respectively, a possible image‐processing application of the method (where a parallel processing is highly desirable) and a comparison with another method proposed in the literature on CNNs. Copyright © 2001 John Wiley & Sons, Ltd.     

Cellular non‐linear networks for minimization of functionals. Part 1: Theoretical aspects

A method for the definition of cellular non‐linear networks able to find approximate minima of rather a large class of continuous functionals is proposed and discussed from a theoretical point of view. The method is based on the spatial discretization of continuous functionals and on the theory of potential functions for resistive circuits. The discretization of the continuous functionals is obtained by resorting to the finite difference method or to the finite element method. The spatial discretization converts a functional into a function of a finite set of variables. By exploiting the theory of potential functions for resistive circuits, from such a function one can derive a lumped circuit that makes it possible to find an approximate minimum of the given functional. Copyright © 2001 John Wiley & Sons, Ltd.     

Indirect adaptive control of nonlinear system via dynamic multilayer neural networks with multi‐time scales

This paper deals with adaptive nonlinear identification and trajectory tracking problem via dynamic multilayer neural network with different time scales. By means of a Lyapunov‐like analysis, we determine stability conditions for the on‐line identification. Then, a sliding mode controller is designed for trajectory tracking with consideration of the modeling error and disturbance. The main contributions of the paper lie in the following aspects. First, we extend our prior identification results of single‐layer dynamic neural networks with multi‐time scales to those of multilayer case. Second, the e‐modification in standard use in adaptive control is introduced in the on‐line update laws to guarantee bounded weights and bounded identification errors. Third, the potential singularity problem in controller design is solved by using new update laws for the NN weights so that the control signal is guaranteed bounded. The stability of proposed controller is proved by using Lyapunov function. Simulation results demonstrate the effectiveness of the proposed algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

Existence and uniqueness of solutions for DC networks containing non‐linear ideal op amps

In this paper we are concerned with networks obtained by connecting independent sources, linear resistors and non‐linear ideal op amps. A necessary and sufficient condition for the existence and uniqueness of solutions for every positive output saturation voltage of the op amps and every value of the independent sources is found. Copyright © 2001 John Wiley & Sons, Ltd.     

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.     

Evaluation and comparison of GHz‐range LC oscillators using time‐varying root‐locus

This paper presents a comprehensive comparison between complementary metal‐oxide‐semiconductor (CMOS) LC‐oscillator topologies often used in GHz‐range transceivers. The comparison utilizes the time‐varying root‐locus (TVRL) method to add new insights into the operation of different oscillators. The paper focuses on the treatment of the TVRL trajectories obtained for different oscillators and establishes links between the trajectories and physical phenomena in oscillators. The evaluation of the root trajectories shows the advantages of the TVRL method for comparing oscillator topologies, which is also extended towards the analysis of voltage‐controlled oscillators. The necessary circuit simplifications required in closed‐form root‐locus analysis are avoided by the TVRL, which allows precise oscillator comparison and reveals details on the topology specifics. The derived conclusions have been verified by the Cadence Spectre‐RF simulator on 130‐nm CMOS process. Copyright © 2011 John Wiley & Sons, Ltd.     

Optimization of power‐efficient wireless mesh networks in outdoor and indoor environments in Japan

This paper proposes the optimization of power‐efficient wireless mesh networks in outdoor and indoor environments, particularly for gas metering in Japan. We investigate the effect on total power consumption by changing the output power and allocated frequency band of a gas metering system. From the simulation results, we show that these changes are effective in achieving low power consumption. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

数字化变电站通信网络的组建与冗余方案

根据站级网络与过程网络是否联通,提出了独立过程网络和全站唯一网络2种数字化变电站通信网络的组建方案。通过分析各自优缺点,指出独立过程网络目前较易实现,而全站唯一网络可先在规模不大、节点数目较少的新建中低压变电站中尝试,待积累一定组网和运行管理经验后,全站唯一网络将凭借信息高度共享等优势成为数字化变电站通信网络的最终形态。详细分析并比较了各种网络冗余技术在数字化变电站通信网络中的应用方法,包括由交换机实现环网冗余协议、快速生成树协议和链路聚合控制协议,由智能电子设备(IED)实现双网口热备用和双网口独立工作等;最后提出了当前实施网络冗余可能存在的问题。     

Enhanced result on stability analysis of randomly occurring uncertain parameters,leakage, and impulsive BAM neural networks with time‐varying delays: Discrete‐time case

In real‐world problems, neural networks play an increasingly important role in terms of both theory and applications. In this paper, the asymptotic stability analysis issue is investigated for uncertain impulsive discrete‐time bidirectional associative memory neural networks with leakage and time‐varying delays. With the assistance of novel summation inequality, reciprocally convex combination technique, and triple Lyapunov‐Krasovskii functionals terms, many cases of time‐varying delays are examined to certify the stability of neural networks. Here, the uncertainties are considered as a randomly occurring parameter uncertainty, and it conforms certain mutually uncorrelated Bernoulli‐distributed white noise sequences. An important feature of the results reported here is that the probability of occurrence of the parameter uncertainties specify a priori estimate. Finally, numerical examples are proposed to expose the capability and efficiency of our research work with the help of the LMI control toolbox in MATLAB.     

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.     

Novel stability criteria of Cohen–Grossberg neural networks with time‐varying delays

In this paper, a class of Cohen–Grossberg neural networks with time‐varying delays is investigated. Based on several new Lyapunov–Krasovskii functionals, by employing the homeomorphism mapping principle, the Halanay inequality, a nonlinear measure approach and linear matrix inequality techniques, several delay‐independent sufficient criteria are obtained for the existence, uniqueness and globally exponential stability of considered neural networks. Without assuming the boundedness and monotonicity of activation functions, the obtained conditions generalize some previous results in the literature. Two examples are also given to show the less conservativeness of the obtained conditions. 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.     

M‐matrices and global convergence of discontinuous neural networks

The paper considers a general class of neural networks possessing discontinuous neuron activations and neuron interconnection matrices belonging to the class of M‐matrices or H‐matrices. A number of results are established on global exponential convergence of the state and output solutions towards a unique equilibrium point. Moreover, by exploiting the presence of sliding modes, conditions are given under which convergence in finite time is guaranteed. In all cases, the exponential convergence rate, or the finite convergence time, can be quantitatively estimated on the basis of the parameters defining the neural network. As a by‐product, it is proved that the considered neural networks, although they are described by a system of differential equations with discontinuous right‐hand side, enjoy the property of uniqueness of the solution starting at a given initial condition. The results are proved by a generalized Lyapunov‐like approach and by using tools from the theory of differential equations with discontinuous right‐hand side. At the core of the approach is a basic lemma, which holds under the assumption of M‐matrices or H‐matrices, and enables to study the limiting behaviour of a suitably defined distance between any pair of solutions to the neural network. Copyright © 2006 John Wiley & Sons, Ltd.