Nonlinear regression with piecewise affine models based on RBFN

Authors:
Masaru Sakamoto;Dong Duo;Yoshihiro Hashimoto;Toshiaki Itoh
Affiliations:
Department of Systems Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Systems Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Systems Engineering, Nagoya Institute of Technology, Nagoya, Japan;Department of Systems Engineering, Nagoya Institute of Technology, Nagoya, Japan
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

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Abstract

In this paper, a modeling method of high dimensional piecewise affine models is proposed. Because the model interpolates the outputs at the orthogonal grid points in the input space, the shape of the piecewise affine model is easily understood. The interpolation is realized by a RBFN, whose function is defined with max-min functions. By increasing the number of RBFs, the capability to express nonlinearity can be improved. In this paper, an algorithm to determine the number and locations of RBFs is proposed.