Back propagation learning with periodic chaos neurons

Abstract

AbstractThis paper proposes a back propagation method using a periodic activation function for a hierarchical neural network and examines its learning capability. As a specific learning problem, the paper addresses the n‐bit parity problem and conducts a learning process by using a three‐layer neural network. The computer simulation results indicate an increased learning coefficient range achieving a 100% learning success rate with an improved speed of learning compared with a neural network model using a monotonic activation function. In addition, the new method lessens the influence of the initial value that is set at the beginning of a learning process, and reduces the degree of dependency on parameters. © 2002 Wiley Periodicals, Inc. Electron Comm Jpn Pt 3, 86(3): 11–19, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjc.1136