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
Absolute stability of analytic neural networks: an approach based on finite trajectory length
Mauro Forti,Alberto Tesi +1 more
TL;DR: In this paper, the authors prove that the total length of the NN trajectories is finite under the assumption that the neural network possesses a Lyapunov function, and the nonlinearities involved (neuron activations, inhibitions, etc.) are modeled by analytic functions.
Journal ArticleDOI
Cellular neural networks to explore complexity
TL;DR: A unifying approach to pattern formation and active wave propagation phenomena is presented and it is proven that both of these behaviours can be simulated with CNNs with the same cell structure, and the thoroughly different dynamics can arise only suitably modulating the CNN cell parameters.
Journal ArticleDOI
The CNN Universal Machine is as universal as a Turing Machine
K.R. Crounse,Leon O. Chua +1 more
TL;DR: It is shown that the simplest integrated circuit implementations of the CNN Universal Machine can play the "game of life", and are therefore equivalent to Turing Machines.
Journal ArticleDOI
Stability analysis of stochastic fuzzy cellular neural networks with time-varying delays
Shujun Long,Daoyi Xu +1 more
TL;DR: A new L@?operator inequality is established and using the properties of M@?matrix, under more relaxing condition of the diffusion coefficient matrix, the sufficient condition ensuring the exponential pth moment stability and almost sure exponential stability of stochastic fuzzy cellular neural networks with time-varying delays is obtained.
Journal ArticleDOI
Fixed-time synchronization of memristor-based fuzzy cellular neural network with time-varying delay
Mingwen Zheng,Mingwen Zheng,Lixiang Li,Haipeng Peng,Jinghua Xiao,Yixian Yang,Yanping Zhang,Hui Zhao +7 more
TL;DR: The fixed-time synchronization control of the drive-response MFCNN is converted into the equivalent fixed- time stability problem of the error system between theDrive-response systems by utilizing differential inclusion, set-valued map theory, and the definitions of finite-time and Fixed-time stability.
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.
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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.