Name
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. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_14 | ICANN (2) |
Keywords | Field | DocType |
orthogonal grid point,input space,nonlinear regression,max-min function,piecewise affine model,high dimensional piecewise affine,modeling method | Affine transformation,Applied mathematics,Affine shape adaptation,Radial basis function network,Computer science,Interpolation,Artificial intelligence,Hyperplane,Piecewise linear function,Piecewise,Mathematical optimization,Pattern recognition,Affine combination | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masaru Sakamoto | 1 | 2 | 1.45 |
| Dong Duo | 2 | 0 | 0.34 |
| Yoshihiro Hashimoto | 3 | 15 | 3.49 |
| Toshiaki Itoh | 4 | 9 | 3.45 |