Journal ArticleDOI
Cellular neural networks: theory
Leon O. Chua,L. Yang +1 more
Reads0
Chats0
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
More filters
Journal ArticleDOI
Gabor-type filtering in space and time with cellular neural networks
TL;DR: The design of cellular neural networks (CNNs) which compute the outputs of filters similar to Gabor filters are described, which might eventually relieve the computational bottleneck associated with Gabor filtering image-processing algorithms.
Journal ArticleDOI
CNN cloning template: hole-filler
TL;DR: A CNN (cellular neural network) template for hole-filling is reported and there are many holes in Japanese characters and the CNN described can be used in addition to a connected-component-detector CNN for Japanese character recognition.
Journal ArticleDOI
Cellular neural networks: Theory and circuit design
TL;DR: An abstract normalized definition of cellular neural networks with arbitrary interconnection topology is given and the property of convergence is found to be of central importance: large classes of convergent CNNs in practice always asymptotically approach some stable equilibrium where each component of the corresponding output is binary-valued.
Book
Stability of dynamical systems
Xiaoxin Liao,Liqiu Wang,Pei Yu +2 more
TL;DR: This monograph provides some state-of-the-art expositions of major advances in fundamental stability theories and methods for dynamic systems of ODE and DDE types and in limit cycle, normal form and Hopf bifurcation control of nonlinear dynamic systems.
Proceedings ArticleDOI
Fast, reliable, adaptive, bimodal people tracking for indoor environments
TL;DR: A real-time system for a mobile robot that can reliably detect and track people in uncontrolled indoor environments and applications include in particular service robots for social events are presented.
References
More filters
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
TL;DR: The purpose and nature of Biological Memory, as well as some of the aspects of Memory Aspects, are explained.
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.