Complex-valued multilayer perceptron search utilizing eigen vector descent and reducibility mapping

Abstract

A complex-valued multilayer perceptron (MLP) can approximate a periodic or unbounded function, which cannot be easily realized by a real-valued MLP. Its search space is full of crevasse-like forms having huge condition numbers; thus, it is very hard for existing methods to perform efficient search in such a space. The space also includes the structure of reducibility mapping. The paper proposes a new search method for a complex-valued MLP, which employs both eigen vector descent and reducibility mapping, aiming to stably find excellent solutions in such a space. Our experiments showed the proposed method worked well. © 2012 Springer-Verlag.