Designing templates for cellular neural networks using particle swarm optimization

Abstract

Designing or learning of templates for cellular neural networks constitutes one of the crucial research problems of this paradigm. In this work, we present the use of a particle swarm optimizer, a global search algorithm, to design a template set for a CNN. A brief overview of the algorithms and methods is given. Design of popular templates is performed using the search algorithm described.