Table of Contents
Advances in Artificial Neural Systems
Volume 2015 (2015), Article ID 931379, 16 pages
http://dx.doi.org/10.1155/2015/931379
Research Article

Stochastic Search Algorithms for Identification, Optimization, and Training of Artificial Neural Networks

Faculty of Management, 21000 Novi Sad, Serbia

Received 6 July 2014; Revised 19 November 2014; Accepted 19 November 2014

Academic Editor: Ozgur Kisi

Copyright © 2015 Kostantin P. Nikolic. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents certain stochastic search algorithms (SSA) suitable for effective identification, optimization, and training of artificial neural networks (ANN). The modified algorithm of nonlinear stochastic search (MN-SDS) has been introduced by the author. Its basic objectives are to improve convergence property of the source defined nonlinear stochastic search (N-SDS) method as per Professor Rastrigin. Having in mind vast range of possible algorithms and procedures a so-called method of stochastic direct search (SDS) has been practiced (in the literature is called stochastic local search-SLS). The MN-SDS convergence property is rather advancing over N-SDS; namely it has even better convergence over range of gradient procedures of optimization. The SDS, that is, SLS, has not been practiced enough in the process of identification, optimization, and training of ANN. Their efficiency in some cases of pure nonlinear systems makes them suitable for optimization and training of ANN. The presented examples illustrate only partially operatively end efficiency of SDS, that is, MN-SDS. For comparative method backpropagation error (BPE) method was used.