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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Journal ArticleDOI
Complex projective synchronization of complex-valued neural network with structure identification
Hao Zhang,Xing-yuan Wang +1 more
TL;DR: In this study, unknown network structure and time-varying delays are considered and the network structure will be identified and the problem of bounded time delays can be solved.
Journal ArticleDOI
Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple discrete and distributed time-varying delays
TL;DR: A novel linear matrix inequality (LMI) based stability criterion is derived to guarantee the asymptotic stability of stochastic cellular neural networks with multiple discrete and distributed time varying delays which are represented by T–S fuzzy models.
Journal ArticleDOI
Decentralized Asynchronous Learning in Cellular Neural Networks
TL;DR: A decentralized asynchronous learning (DAL) framework for CNNs is developed in which each cell of the CNN learns in a spatially and temporally distributed environment and an application of DAL framework is demonstrated by developing a CNN-based wide-area monitoring system for power systems.
Journal ArticleDOI
Complete stability in multistable delayed neural networks
Chang-Yuan Cheng,Chih-Wen Shih +1 more
TL;DR: A new formulation modified from the previous studies on multistable networks is developed to derive componentwise dynamical property and it is concluded that every solution of the network converges to a single equilibrium as time tends to infinity.
Proceedings ArticleDOI
A learning algorithm for cellular neural networks (CNN) solving nonlinear partial differential equations
F. Puffer,R. Tetzlaff,D. Wolf +2 more
TL;DR: The results show that - depending on the training pattern - solutions of various PDE can be approximated with high accuracy by a simple CNN structure.
References
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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.