Using parallel random search to train fuzzy neural networks
Аndrii Oliinyk, Stepan Skrupsky, С. А. Субботин · Automatic Control and Computer Sciences · 2014
A solution to the problem of training fuzzy neural networks is considered. A method of parametric identification of fuzzy neural models that is based on a probabilistic approach when searching for the values of adjustable parameters is proposed. The method allocates the most resource-intensive stages among nodes of a parallel computing system, which reduces the time it takes to adjust the parameters. It is proposed to take into account information on the training sample when forming the initial set of solutions and significance of terms of features, which brings the initial points closer to optimal and accelerates the optimization process.