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. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Sakamoto, M., Duo, D., Hashimoto, Y., & Itoh, T. (2005). Nonlinear regression with piecewise affine models based on RBFN. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3697 LNCS, pp. 85–90). https://doi.org/10.1007/11550907_14
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