Journal ArticleDOI
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
Citations
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Finite-time synchronization of multi-coupling stochastic fuzzy neural networks with mixed delays via feedback control
Dongsheng Xu,Yu Liu,Ming Liu +2 more
TL;DR: In this article, a finite-time synchronization problem for multi-coupling fuzzy cellular neural networks (FCNNs) with stochastic perturbations and mixed delays is studied for the first time, which is of better significance.
Journal ArticleDOI
A new method to analyze complete stability of pwl cellular neural networks
Mauro Forti,Alberto Tesi +1 more
TL;DR: A novel approach for studying complete stability of piecewise-linear (PWL) CNN's based on a fundamental limit theorem for the length of the CNN trajectories, which shows that complete stability holds under hypotheses weaker than those considered in Chua & Yang, 1988.
Journal ArticleDOI
Existence of periodic oscillatory solution of reaction-diffusion neural networks with delays
Hongyong Zhao,Guanglan Wang +1 more
TL;DR: In this paper, a class of reaction-diffusion cellular neural networks with delays was studied and sufficient conditions for the existence, uniqueness, and global exponential stability of the periodic oscillatory solution were obtained.
Journal ArticleDOI
Cellular neural networks for real-time DNA microarray analysis
TL;DR: The results of testing an algorithm based on the CNN universal machine (CNN-UM) that has been designed to classify the image data are discussed, and the algorithm is implemented in an analogic (analog and logic) microprocessor.
Journal ArticleDOI
Markov random field image segmentation using cellular neural network
Tamás Szirányi,Josiane Zerubia +1 more
TL;DR: A pixel-level statistical estimation model for statistical image segmentation using the CNN UM architecture and the Modified Metropolis Dynamics (MMD) method, which can be implemented into the raw analog architecture of the CNN.
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.
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Self Organization And Associative Memory
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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.