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System identification in nonstationary environment represents a challenging problem and an advaned neural architecture namely Time-Varying Neural Net- works (TV-NN) has shown remarkable identification properties in nonlinear and nonstationary conditions. Time-varying weights, each being a linear com- bination of a certain set of basis functions, are used in such kind of networks instead of stable ones, which inevitalbly increases the number of free parame- ters. Therefore, an Extreme Learning Machine (ELM) approach is developed to accelerate the training procedure for TV-NN. What is more, in order to ob- tain a more compact structure, or determine several important parameters, or update the network more efficiently in online case, several variants of ELM-TV are proposed and discussed in the book. Related computer simulations have been carried out and show the effectiveness of the algorithms.
| Autorius: | Yibin Ye, Stefano Squartini, Francesco Piazza, |
| Leidėjas: | LAP LAMBERT Academic Publishing |
| Išleidimo metai: | 2012 |
| Knygos puslapių skaičius: | 88 |
| ISBN-10: | 3659248908 |
| ISBN-13: | 9783659248900 |
| Formatas: | 220 x 150 x 6 mm. Knyga minkštu viršeliu |
| Kalba: | Anglų |
Parašykite atsiliepimą apie „ELM in nonstationary environment: Extreme Learning Machine and its variants for Time-Varying Neural Networks case study“
