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
Genetic algorithm for CNN template learning
TL;DR: In this paper, a learning algorithm for space invariant cellular neural networks (CNNs) is described, which is formulated as an optimization problem and derived using a genetic optimization algorithm.
Journal ArticleDOI
LMI optimization approach on stability for delayed neural networks of neutral-type
TL;DR: A novel delay-dependent criterion for the stability of delayed cellular neural networks of neutral-type using the Lyapunov stability theory and linear matrix inequality (LMI) framework is presented.
Journal ArticleDOI
Robust stability of cellular neural networks with delay: linear matrix inequality approach
TL;DR: In this paper, a criterion for the global asymptotic stability and uniqueness of the equilibrium point of uncertain cellular neural networks with delay is presented, where uncertainties are assumed to be norm-bounded.
Journal ArticleDOI
A new stability criterion for bidirectional associative memory neural networks of neutral-type
TL;DR: In the paper, the global asymptotic stability of equilibrium is considered for continuous bidirectional associative memory (BAM) neural networks of neutral type by using the Lyapunov method in terms of linear matrix inequality (LMI).
Journal ArticleDOI
Bifurcation and Chaos in Noninteger Order Cellular Neural Networks
TL;DR: In this article, a new class of Cellular Neural Networks (CNNs) is introduced, which consists in replacing the traditional first order cell with a noninteger order one, leading to the onset of chaos in a two-cell system of a total order of less than three.
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.
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.