How complex-valued multilayer perceptron can predict the behavior of deterministic chaos

Abstract

A complex-valued multilayer perceptron has the capability to represent complicated periodicity. We employ a very powerful learning method called C-SSF for learning a complex-valued multilayer perceptron. C-SSF finds a series of excellent solutions through successive learning. In deterministic chaos, long-term prediction is considered impossible. We apply C-SSF to two kinds of deterministic chaos and evaluate the learning and prediction performance of C-SSF.