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.
  • ISBN13: 9783659248900
  • Publisher: LAP Lambert Academic Publishing
  • Pubilcation Year: 2012
  • Format: Paperback
  • Pages: 00088
Dimensions5.9 x 0.2 x 8.7 inches
Publication Date20121109

ELM in Nonstationary Environment

Ye Yibin

Write a Review

Free Shipping over $35 and Free Returns 

$0.41 off if you opt out of free returns