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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The use of CNN models in the subcortical visual pathway. (Reseach report of the Dual and Neural Computing Systems Laboratory DNS-16-1992)
Tamás Roska,József Hámori,E. Lábos,K. Lotz,László Orzó,J. Takács,P.L. Venetianer,Zoltán Vidnyánszky,Ákos Zarándy +8 more
Journal ArticleDOI
Convergence of gradient method with momentum for two-Layer feedforward neural networks
TL;DR: This work proves the weak and strong convergence results, as well as the convergence rates for the error function and for the weight, of a gradient method with momentum for two-layer feedforward neural networks with momentum set to be a constant.
Journal ArticleDOI
A new synthesis approach for feedback neural networks based on the perceptron training algorithm
Derong Liu,Z. Lu +1 more
TL;DR: A new synthesis approach is developed for associative memories based on the perceptron training algorithm that is guaranteed to converge for the design of neural networks without any constraints on the connection matrix.
Journal ArticleDOI
An intelligent system for wafer bin map defect diagnosis: An empirical study for semiconductor manufacturing
C. M. Liu,Chen-Fu Chien +1 more
TL;DR: The proposed WBM intelligent system can recognize specific failure patterns efficiently and also record the assignable root causes verified by the domain experts to enhance troubleshooting effectively.
PatentDOI
Graphene-based non-boolean logic circuits
TL;DR: In this article, a dual-gate transistor with negative differential resistance (NDR) region is described and a dynamic bias controller configured to simultaneously sweep a source-drain voltage and a top-gate voltage across a Dirac point to provide operation within the NDR region.
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.