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.
Highlights
An artificial neural network (ANN) is an analysis paradigm that is a simple model of the brain and the back-propagation algorithm is the one of the most popular method to train the artificial neural network
Attempts to speed up training and reduce convergence to local minima have been made in the context of gradient descent[3,4,5]
A new approach is proposed for the convergence of associative neural memories by using the Fuzzy Particle Swarm Optimization technique (FPSO)
Summary
An artificial neural network (ANN) is an analysis paradigm that is a simple model of the brain and the back-propagation algorithm is the one of the most popular method to train the artificial neural network. Particle swarm optimization (PSO) is a population based stochastic optimization technique[6,7] inspired by social behavior of bird flocking or fish schooling This is modeled by particles in multidimensional space that have a position and a velocity. If the velocity exceeded this threshold, it was set equal to Vmax This parameter is proved to be crucial, because large values could results in the particles moving past good solutions, while small values could result in insufficient exploration of the search space. The by applying fuzzy c-mean algorithm to attain the velocity (position change) of this particle can be neighborhood best and we reduce the number of represented by another D-dimensional vector Vi = (vi, vi2,...,viD). 0.4 using 3 and 4, until a convergence criterion is reached
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