Title :
PSO-Based Cloning Template Design for CNN Associative Memories
Author :
Giaquinto, A. ; Fornarelli, G.
Author_Institution :
Dipt. di Elettrotec. ed Elettron., Politec. di Bari, Bari, Italy
Abstract :
In this brief, a synthesis procedure for cellular neural networks (CNNs) with space-invariant cloning templates is proposed. The design algorithm is based on the use of the evolutionary algorithm of the particle swarm optimization (PSO) with the application to associative memories. The proposed synthesis procedure takes into account requirements in terms of robustness to parametric variations. Numerical results show that the networks also guarantee good performances in terms of correct recall in the presence of noisy patterns.
Keywords :
cellular neural nets; content-addressable storage; evolutionary computation; particle swarm optimisation; PSO; associative memory; cellular neural network; evolutionary algorithm; parametric variation; particle swarm optimization; space-invariant cloning template design; Associative memories; cellular neural networks (CNNs); particle swarm optimization (PSO); robustness to parametric variations; Algorithms; Artificial Intelligence; Association Learning; Computer Simulation; Mathematical Computing; Mathematical Concepts; Memory; Neural Networks (Computer);
Journal_Title :
Neural Networks, IEEE Transactions on
DOI :
10.1109/TNN.2009.2031870