An Adaptively Constructing Multilayer Feedforward Neural Networks Using Hermite Polynomials

Authors:
L. Ma;K. Khorasani
Affiliations:
Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Japan 243-0297;Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8
Venue:
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Year:
2008

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Abstract

In this paper a new strategy is introduced for constructing a multi-hidden-layer feedforward neural network (FNN) where each hidden unit employs a polynomial function for its activation function that is different from other units. The proposed scheme incorporates a structure level adaptation as well as a function level adaptation methodologies in constructing the desired network. The activation functions considered consist of orthonormal Hermite polynomials. Using this strategy, a FNN can be constructed as having as many hidden layers and hidden units as dictated by the complexity of the problem being considered.