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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
Citations
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Exponential input-to-state stability of stochastic Cohen–Grossberg neural networks with mixed delays
TL;DR: In this article, the authors studied the input-to-state stability analysis for a class of impulsive stochastic Cohen-Grossberg neural networks with mixed delays and obtained sufficient conditions to ensure that the considered system with/without impulse control is mean-square exponentially stable.
Journal ArticleDOI
Global Mittag-Leffler stability and synchronization of impulsive fractional-order neural networks with time-varying delays
TL;DR: In this paper, the authors considered a class of impulsive Caputo fractional-order cellular neural networks with time-varying delays and provided sufficient conditions for global Mittag-Leffler stability.
Journal ArticleDOI
Algebraic criteria for global exponential stability of cellular neural networks with multiple time delays
Xiaoxin Liao,Jun Wang +1 more
TL;DR: In this paper, three sufficient conditions for the global exponential stability of cellular neural networks with time delays are presented, which provide algebraic criteria for stability verifications and improve upon existing ones with stronger conditions.
Journal ArticleDOI
Stability and Dissipativity Analysis of Distributed Delay Cellular Neural Networks
Zhiguang Feng,James Lam +1 more
TL;DR: By introducing an integral partitioning technique, two new forms of Lyapunov-Krasovskii functionals are constructed, and improved distributed delay-dependent stability conditions are established in terms of linear matrix inequalities.
Journal ArticleDOI
Current-mode techniques for the implementation of continuous- and discrete-time cellular neural networks
Ángel Rodríguez-Vázquez,S. Espejo,R. Dominguez-Castron,José L. Huertas,Edgar Sanchez-Sinencio +4 more
TL;DR: In this paper, a unified, comprehensive approach to the design of continuous-time and discrete-time cellular neural networks (CNNs) using CMOS current-mode analog techniques is presented.
References
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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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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.