ELM in nonstationary environment - Extreme Learning Machine and its variants for Time-Varying Neural Networks case study

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.

About the author

Yibin Ye was born in Guangdong, China, on January 10, 1981. He received the B.S. degree in electronic and information engineering in 2003 at South China University of Technology, and Ph.D. degree at Universita Politecnica delle Marche (Italy) in 2012. His research interests include wireless networks and machine learning.

Product details

• Authors: Francesco Piazza, Stefan Squartini, Stefano Squartini, Yibi Ye, Yibin Ye
• Publisher: LAP Lambert Academic Publishing
• Languages: English, German
• Product format: Paperback / Softback
• Released: 13.11.2012
• EAN: 9783659248900
• ISBN: 978-3-659-24890-0
• No. of pages: 88
• Dimensions: 150 mm x 220 mm x 5 mm
• Weight: 150 g
• Subjects: Guides; Natural sciences, medicine, IT, technology > IT, data processing > Miscellaneous