Memory Convergence and Optimization with Fuzzy PSO and ACS

Abstract

Associative neural memories are models of biological phenomena that allow for the storage of pattern associations and the retrieval of the desired output pattern upon presentation of a possibly noisy or incomplete version of an input pattern. In this study, we introduce fuzzy swarm particle optimization technique for convergence of associative neural memories based on fuzzy set theory. A Fuzzy Particle Swarm Optimization (FPSO) consists of clustering of swarm's particle by applying fuzzy c-mean algorithm to attain the neighborhood best. We present a singular value decomposition method for the selection of efficient rule from a given rule base required to attain the global best. Finally, we illustrate the proposed method by virtue of some examples. Further, ant colony system ACS algorithm is used to study the Symmetric Traveling Salesman Problem TSP. The optimum parameters for this algorithm have to found by trial and error. The ACS parameters working in a designed subset of TSP instances has also been optimized by virtue of Particle Swarm Optimization PSO.