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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Journal ArticleDOI
Image processing using cellular neural networks based on multi-valued and universal binary neurons
TL;DR: In this paper, the authors propose a rapid convergence learning algorithm for universal binary neurons (UBNs) and multi-valued neurons (MVNs) to solve image processing and image analysis problems.
Proceedings ArticleDOI
SCNN 2000. I. Basic structure and features of the simulation system for cellular neural networks
A. Loncar,R. Kunz,R. Tetzlaff +2 more
TL;DR: The basic structure and features of SCNN2000, a universal simulation system for cellular neural networks (CNN) is presented and a new SCNN control system has been developed, including a new graphical user interface and an integrated SCNN shell to allow a more convenient working with SCNN 2000.
Proceedings ArticleDOI
Cellular neural networks as a model of associative memories
S. Tan,J. Hao,Joos Vandewalle +2 more
TL;DR: A discrete-time version of cellular neural nets featuring simple linear thresholding neurons and the synchronous state-updating rule is considered and the Hebbian rule is adopted as the memory design rule.
Proceedings ArticleDOI
A new approach to emulate CNN on FPGAs for real time video processing
K. Kayaer,V. Tavsanoglu +1 more
TL;DR: A new processor architecture implementing the Discrete Time Cellular Neural Networks (DT-CNN) on FPGA is proposed, which intends to process video images real time with 3times3 CNN templates and without the use of an external memory.
Journal ArticleDOI
New LMI conditions for global exponential stability of cellular neural networks with delays
TL;DR: In this article, the authors investigated further cellular neural networks model with delays and used linear matrix inequality framework and Homeomorphism theorem to prove the existence and uniqueness of the equilibrium, and derive some new sufficient condition ensuring global exponential stability of the networks.
References
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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.