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

 
9783659248900: 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.

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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.

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Book Description Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | 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. | Format: Paperback | Language/Sprache: english | 88 pp. Bookseller Inventory # K9783659248900

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Book Description LAP Lambert Academic Publishing Jun 2014, 2014. Taschenbuch. Book Condition: Neu. Neuware - 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. 88 pp. Englisch. Bookseller Inventory # 9783659248900

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Book Description LAP Lambert Academic Publishing Jun 2014, 2014. Taschenbuch. Book Condition: Neu. Neuware - 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. 88 pp. Englisch. Bookseller Inventory # 9783659248900

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Book Description LAP Lambert Academic Publishing, United States, 2014. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****.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. Bookseller Inventory # AAV9783659248900

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Book Description LAP Lambert Academic Publishing Jun 2014, 2014. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - 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. 88 pp. Englisch. Bookseller Inventory # 9783659248900

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