Presentation 1996/2/3
A solution of nonlinear programming problems by fluctuation driven multi-layered neural networks
Masanori SANADA, Takahumi OOHORI, Kazuhisa WATANABE,
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Abstract(in English) We propose a process to solve nonlinear programing problems by the fluctuation-driven multi-layered neural networks(NN). Going by the fluctuation-driven learning(FDL) rule, the connection weights to each neuron are modified by time averages of product sequences of the output errors of NN, the fluctuation in the neuron and the outputs from the presynaptic neurons. The solution process consists of two phases, function-learning phase and variable-optimizing phase. In the former, the FDL rule makes the NN learn the objectiv function. In the latter, the FDL rule with 'slicing-off' operation modifies the NN's input values toward the global minimum across the local minima. Simulation results show the expected performances.
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Keyword(in English) fluctuation-driven learning rule / multi-layered NN / nonlinear programming problem / steepest descent method
Paper # NC95-107
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Committee NC
Conference Date 1996/2/3(1days)
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Registration To Neurocomputing (NC)
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A solution of nonlinear programming problems by fluctuation driven multi-layered neural networks
Sub Title (in English)
Keyword(1) fluctuation-driven learning rule
Keyword(2) multi-layered NN
Keyword(3) nonlinear programming problem
Keyword(4) steepest descent method
1st Author's Name Masanori SANADA
1st Author's Affiliation Department of Electrical Engineering, Hokkaido Institute of Technology()
2nd Author's Name Takahumi OOHORI
2nd Author's Affiliation Department of Electrical Engineering, Hokkaido Institute of Technology
3rd Author's Name Kazuhisa WATANABE
3rd Author's Affiliation Department of Electrical Engineering, Hokkaido Institute of Technology
Date 1996/2/3
Paper # NC95-107
Volume (vol) vol.95
Number (no) 506
Page pp.pp.-
#Pages 7
Date of Issue