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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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Globally exponential stability condition of a class of neural networks with time-varying delays
TL;DR: Based on the Lyapunov stability method, a novel and less conservative exponential stability condition is derived in this paper, which is delay-dependent and easily applied only by checking the Hamiltonian matrix with no eigenvalues on the imaginary axis instead of directly solving an algebraic Riccati equation.
Journal ArticleDOI
Exponential lag synchronization of fuzzy cellular neural networks with time-varying delays ☆
Zhiwei Xing,Jigen Peng +1 more
TL;DR: Some sufficient conditions on the exponential lag synchronization of the FCNNs are obtained using a nonlinear measure method and the exponential decay rate of synchronization error is estimated.
Journal ArticleDOI
Magnetic cellular nonlinear network with spin wave bus for image processing
TL;DR: In this article, the authors describe and analyze a cellular nonlinear network based on magnetic nanostructures for image processing, which consists of magneto-electric cells integrated onto a common ferromagnetic film-spin wave bus.
Journal ArticleDOI
The new framework of applications: the Aladdin system
TL;DR: One of the most important features of the Aladdin system is the image processing library, which reduces algorithm development time, provides efficient codes, error free operation in binary, and accurate operation in grayscale nodes.
Journal ArticleDOI
Multiperiodicity of Periodically Oscillated Discrete-Time Neural Networks With Transient Excitatory Self-Connections and Sigmoidal Nonlinearities
TL;DR: Using transient excitatory self-interactions of neurons and sigmoidal nonlinearities, an approach is developed to investigate multiperiodicity and attractivity of periodically oscillated DTNNs with time-varying and distributed delays and shows that, under some new criteria, there exist multiplicity results of periodic solutions which are locally or globally exponentially stable.
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
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.