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
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Proceedings ArticleDOI
Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-end Robot Learning Scheme
TL;DR: A new regularization loss component is introduced together with a learning algorithm that improves the stability of the learned autonomous system, by forcing the eigenvalues of the internal state updates of an LDS to be negative reals.
Journal ArticleDOI
CNN template robustness with different output nonlinearities
Ari Paasio,Adam Dawidziuk +1 more
TL;DR: In this paper, the robustness of the templates used in the normal unity gain operation is discussed, and some methods to improve a possibly poor template are given, and the positive range type of the cell output non-linearity and the templates in that particular case are examined.
Journal ArticleDOI
Measure synchronization in coupled Duffing Hamiltonian systems
TL;DR: In this paper, the collective behaviour of coupled Duffing Hamiltonian systems was examined and the existence of measure synchronization (MS) in quasi-periodic and chaotic states was shown.
Journal ArticleDOI
Review Neuro-fuzzy computing for image processing and pattern recognition
Sankar K. Pal,Ashish Ghosh +1 more
TL;DR: The relevance of integration of the merits of fuzzy set theory and neural network models for designing an efficient decision making system is explained and feasibility of such systems and different ways of integration are described.
References
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Journal ArticleDOI
Neural networks and physical systems with emergent collective computational abilities
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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.