MaxMin-SOMO: an SOM optimization algorithm for simultaneously finding maximum and minimum of a function

Authors:
Wu Wei;Atlas Khan
Affiliations:
Department of Applied Mathematics, Dalian University of Technology, Dalian, China;Department of Applied Mathematics, Dalian University of Technology, Dalian, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

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Abstract

An self-organizing feature map optimization (SOMO) algorithm was proposed by Mu-Chun Su et al [1,2] in order to find a wining neuron in the SOM network, through a competitive learning process, that stands for the minimum of an objective function. In this study, we generalizes the SOMO algorithm to a so called MaxMin-SOMO algorithm for simultaneously finding two winning neurons such that one winner stands for the minimum and the other for the maximum of the objective function. Numerical simulations show that, when we are interested in both maximum and minimum of an objective function, the MaxMin-SOMO algorithm works more effectively than SOMO algorithm.