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Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
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TLDR
In this article, a class of information processing systems called cellular neural networks (CNNs) are proposed, which consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly through their nearest neighbors.Abstract:
A novel class of information-processing systems called cellular neural networks is proposed. Like neural networks, they are large-scale nonlinear analog circuits that process signals in real time. Like cellular automata, they consist of a massive aggregate of regularly spaced circuit clones, called cells, which communicate with each other directly only through their nearest neighbors. Each cell is made of a linear capacitor, a nonlinear voltage-controlled current source, and a few resistive linear circuit elements. Cellular neural networks share the best features of both worlds: their continuous-time feature allows real-time signal processing, and their local interconnection feature makes them particularly adapted for VLSI implementation. Cellular neural networks are uniquely suited for high-speed parallel signal processing. >read more
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Synchronization in complex delayed dynamical networks with nonsymmetric coupling
Jianshe Wu,Licheng Jiao +1 more
TL;DR: In this paper, a new general complex delayed dynamical network model with nonsymmetric coupling is introduced, and several synchronization criteria for delay-independent and delay-dependent synchronization are provided which generalize some previous results.
Journal ArticleDOI
Global stability of cellular neural networks with constant and variable delays
TL;DR: New conditions ensuring global asymptotic stability and global exponential stability for cellular neural networks with constant delay and variable delay are given by using the essence of piecewise linearity of the output function of cellular Neural networks and constructing Lyapunov functions and functionals.
Journal ArticleDOI
A novel delay-dependent criterion for delayed neural networks of neutral type
TL;DR: In this paper, a robust stability analysis method for delayed neural networks of neutral type was proposed by constructing a new Lyapunov functional, and a novel delay-dependent criterion for the stability was derived in terms of LMIs (linear matrix inequalities).
Journal ArticleDOI
New results for global stability of a class of neutral-type neural systems with time delays
Ruya Samli,Sabri Arik +1 more
TL;DR: It is shown that the results presented in this paper for neutral-type delayed neural networks are the generalization of a recently reported stability result.
Journal ArticleDOI
An approach to information propagation in 1-D cellular neural networks. II. Global propagation
TL;DR: This second of two papers studies how and when a global propagation of information is possible through a one-dimensional (1-D) Cellular Neural Network (CNN) with connections between nearest neighbors only.
References
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Neural networks and physical systems with emergent collective computational abilities
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Neurons with graded response have collective computational properties like those of two-state neurons.
TL;DR: A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied and collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons are studied.
Book
Neurons with graded response have collective computational properties like those of two-state neurons
TL;DR: In this article, a model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied, which has collective properties in very close correspondence with the earlier stochastic model based on McCulloch--Pitts neurons.
Journal ArticleDOI
Neural computation of decisions in optimization problems
John J. Hopfield,David W. Tank +1 more
TL;DR: Results of computer simulations of a network designed to solve a difficult but well-defined optimization problem-the Traveling-Salesman Problem-are presented and used to illustrate the computational power of the networks.