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
Global exponential stability in Lagrange sense for recurrent neural networks with time delays
TL;DR: By constructing appropriate Lyapunov-like functions, this paper provides easily verifiable criteria for the boundedness and global exponential attractivity of RNNs, which can be applied to analyze monostable as well as multistable neural networks.
Journal ArticleDOI
Stability of inertial BAM neural network with time-varying delay via impulsive control
TL;DR: By choosing proper variable transformation, the inertial BAM neural networks can be rewritten as first-order differential equations using the Lyapunov functional method and the comparison principle to derive some sufficient conditions guaranteeing the exponential stability of the neural networks under impulsive control.
Journal ArticleDOI
Reaction-diffusion navigation robot control: from chemical to VLSI analogic processors
Andrew Adamatzky,Paolo Arena,A. Basile,Ricardo Carmona-Galan,Ben de Lacy Costello,Luigi Fortuna,Mattia Frasca,Ángel Rodríguez-Vázquez +7 more
TL;DR: A new methodology and experimental implementations for real-time wave-based robot navigation in a complex, dynamically changing environment is introduced and principles of computation in reaction-diffusion (RD) nonlinear active media where autowaves are used for information processing purposes can be considered as RD computing devices.
Journal ArticleDOI
Dynamical behaviors of discrete-time fuzzy cellular neural networks with variable delays and impulses
Qiankun Song,Jinde Cao +1 more
TL;DR: Several simple sufficient conditions checking the global exponential stability and the existence of periodic solutions are obtained for the neural networks and the estimation for exponential convergence rate index is proposed.
Journal ArticleDOI
An analytic method for designing simple cellular neural networks
Leon O. Chua,Patrick Thiran +1 more
TL;DR: A method is proposed for synthesizing cellular neural networks (CNNs) designed for simple applications that leads to a set of inequalities that must be satisfied by the parameters of the cloning template defining the cellular neural network in order to guarantee correct operation for the network.
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.