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Analogies between Cellular Neural Networks and Partial Differential Equations
No full textFrontiers in Artificial Intelligence and Applications Serie
An approach to information propagation in 1-D cellular neural networks-Part I: Local diffusion
No full textThis is the first of two companion papers [1] devoted to a deep analysis of the dynamics of information propagation in the simplest nontrivial Cellular Neural Network (CNN), which is one-dimensional and has connections between nearest neighbors only. We will show that two behaviors are possible: local diffusion of information between neighboring cells and global propagation through the entire array. This paper deals with local diffusion, of which we will first give an accurate definition, before computing the template parameters for which the CNN has this behavior. Next we will compute the number of stable equilibria, before examining the convergence of any trajectory toward them, for three different kinds of boundary conditions: fixed Dirichlet, reflective, and periodic. © 1998 IEEE
An approach to information propagation in 1-D cellular neural networks-Part I: Local diffusion
Full text linkAn approach to information propagation in 1-D cellular neural networks - Part II: Global propagation
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Analogies between Cellular Neural Networks and Partial Differential Equations
No full textFrontiers in Artificial Intelligence and Applications Serie
Comparing Dynamic Behaviour in 1-D CNNs and Reaction-Diffusion-Convection PDEs: a Review
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An Approach to Cellular Neural Networks for Signal Processing: Information Propagation and Circuit Design
No full textPerspective in Neural Computing Serie
Comparing Dynamic Behaviour in 1-D CNNs and Reaction-Diffusion-Convection PDEs: a Review
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