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
Exponential Synchronization of Stochastic Memristive Recurrent Neural Networks Under Alternate State Feedback Control
TL;DR: This paper solves the exponential synchronization problem of two memristive recurrent neural networks with both stochastic disturbance and time-varying delays via periodically alternate state feedback control with novel sufficient conditions derived on the basis of the Lyapunov stability theory.
Journal ArticleDOI
Cellular neural network formed by simplified processing elements composed of thin-film transistors
Mutsumi Kimura,Ryohei Morita,Sumio Sugisaki,Tokiyoshi Matsuda,Tomoya Kameda,Yasuhiko Nakashima +5 more
TL;DR: It is confirmed that the cellular neural network can learn multiple logics even in a small-scale neural network, and it is verified that it can simultaneously recognize multiple simple alphabet letters.
Journal ArticleDOI
On the structure of multi-layer cellular neural networks
TL;DR: This work decouple Y into two subspaces, say Y (1) and Y (2), and gives a necessary and sufficient condition for the existence of factor maps between them, which clarifies, in a TCNN, each layer’s structure.
Journal ArticleDOI
Improved exponential stability criterion for neural networks with time-varying delay
TL;DR: An improved integral inequality is derived which extends the auxiliary function-based integral inequality and a novel Lyapounov-Krasovskii functional with some new integral terms is constructed and a less conservative exponential stability criterion is obtained.
Journal ArticleDOI
A DTCNN universal machine based on highly parallel 2-D cellular automata CAM/sup 2/
T. Ikenaga,T. Ogura +1 more
TL;DR: A DTCNN processing method based on a highly parallel two-dimensional (2-D) cellular automata called CAM/sup 2/ is proposed, which can attain pixel-order parallelism on a single PC board because it is composed of a content addressable memory (CAM).
References
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Journal ArticleDOI
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.
Book
Self Organization And Associative Memory
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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.