Paper:
Language: English:

Studies on Effects of Initialization on Structure Formationand Generalization of Structural Learning with Forgetting

Hiroshi Shiratsuchi^, Hiromu Gotanda^, Katsuhiro Inoue^, and Kousuke Kumamaru^***

Faculty of Engineering, University of the Ryukyus, Nishihara, Okinawa 903-0213, Japan
Kinki University School of Humanity-Oriented Science and Engineering
Faculty of Computer Science and Systems Engineering, Kyushu
Institute of Technology

Received: October 6, 2003

Accepted: July 8, 2004

Keywords: multilayer neural network, initialization, generalization, structural

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.8, No.6 pp. 621-626, 2004

Abstract

In this paper, our proposed initialization for multilayer neural
networks (NN) applies to the structural learning with forgetting.
Initialization consists of two steps: weights of hidden
units are initialized so that their hyperplanes pass through the
center of gravity of an input pattern set, and weights of output units are initialized to zero. Several simulations were performed to study how the initialization effects the structure formation of the NN. From the simulation result, it was confirmed that the initialization gives better network structure and higher generalization ability.

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