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
Stability analysis of generalized cellular neural networks
Cuneyt Guzelis,Leon O. Chua +1 more
TL;DR: The results on the global asymptotic stability are useful for the design of a generalized CNN such that the orbit of each state converges to a globally asymPTotically stable equilibrium point which depends only on the input and not on the initial state.
Journal ArticleDOI
Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations
TL;DR: By utilizing the discontinuous state feedback control method and constructing Lyapunov functionals, new and useful finite-time synchronization criteria for the considered networks are established, which significantly generalize and improve recent works in literature.
Journal ArticleDOI
Self-organization in a two-layer CNN
TL;DR: It is shown that a two-layer cellular neural network with constant templates is suitable for generating self-organizing patterns and, therefore, it is able to model complex phenomena.
Proceedings ArticleDOI
Stability of synchronized distributed control of discrete swarm structures
Kai Jin,Ping Liang,Gerardo Beni +2 more
TL;DR: The authors prove that both 1D and 2D swarm structures are stable with appropriate weights in the sum of adjacent errors if the vertical disturbances vary sufficiently more slowly than the response time of the servo systems of the agents.
Journal ArticleDOI
Complete and lag synchronization of hyperchaotic systems using small impulses
Chuandong Li,Xiaofeng Liao +1 more
TL;DR: In this paper, the complete synchronization and lag synchronization of the hyperchaotic systems are restated as the impulsive control issues, and simple and easy-to-be-verified stability criteria are derived, and then applied to synchronize and lag-synchronize the Chua's oscillators.
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.
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.