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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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Reading complexity in chua's oscillator through music. part i: a new way of understanding chaos
TL;DR: Human cognitive abilities can analyze the large and complicated patterns produced by Chua's systems translated into music, achieving the cognitive economy and the coordination and synthesis of countless data at the authors' disposal that occur in the perception of dynamic events in the real world.
Journal ArticleDOI
Microarray image enhancement by denoising using decimated and undecimated multiwavelet transforms
TL;DR: A new approach to deal with the noise inherent in the microarray image processing procedure is presented, using the denoising capabilities of decimated and undecimated multiwavelet transforms, DMWT and UMWT respectively, for the removal of noise from microarray data.
Journal ArticleDOI
Boundedness and stability for nonautonomous cellular neural networks with delay
TL;DR: A class of nonautonomous cellular neural networks is studied by constructing a suitable Liapunov functional by applying the boundedness theorem for general functional-differential equations and the Banach fixed point theorem.
Journal ArticleDOI
Delay-difference-dependent robust exponential stability for uncertain stochastic neural networks with multiple delays
TL;DR: Based on the multiple-difference-dependent Lyapunov–Krasovskii functional and free-weighting matrices method, some novel stability criteria for the addressed uncertain stochastic neural networks are derived.
Journal ArticleDOI
Technical communique: An improved result for complete stability of delayed cellular neural networks
Qiang Zhang,Xiaopeng Wei,Jin Xu +2 more
TL;DR: This paper provides a new sufficient condition for the complete stability of the delayed cellular neural networks and imposes constraints on the parameters of the networks and the size of time delay.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
TL;DR: A model of a system having a large number of simple equivalent components, based on aspects of neurobiology but readily adapted to integrated circuits, produces a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size.
Book
Self Organization And Associative Memory
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
Journal ArticleDOI
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.