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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Proceedings ArticleDOI
Delay-dependent exponential stability analysis of delayed cellular neural networks
TL;DR: For the delayed cellular neural networks, the estimate of exponential convergence rate and exponential stability is considered in this paper, where the Lyapunov-Krasovskii functionals combined with linear matrix inequality (LMI) approach are employed to investigate the bound on the cell template and delay-type cell template matrices so that the systems are exponentially stable.
Journal ArticleDOI
Delay-dependent and delay-independent stability criteria for cellular neural networks with delays
TL;DR: Several novel delay-dependent and delay-independent asymptotical/exponential stability criteria are established by employing parameterized first-order model transformation, Lyapunov–Krasovskii stability theorem and LMI technique in virtue of the linearization of considered model.
Journal ArticleDOI
Global stability analysis for a class of cohen-grossberg neural network models
TL;DR: By constructing suitable Lyapunov functionals and combin- ing with matrix inequality technique, a new simple sufficient condition is presented for the global asymptotic stability of the Cohen-Grossberg neural network models as discussed by the authors.
Journal ArticleDOI
CMOS implementation of an analogically programmable cellular neural network
TL;DR: It is shown that the designed CNN can be successfully used to perform such useful functions as noise removal, edge detection, hole filling, shadow detection, and connected component recognition.
Journal ArticleDOI
Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays
TL;DR: This paper presents two algebraic criteria for the input-to-state stability of recurrent neural networks with time-varying delays which ensure global exponential stability when the input u(t) is equal to 0 and is easy to be verified only with the connection weights of the recurrent Neural networks.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
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Journal ArticleDOI
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.