Presentation 2002/7/19
Structures of Neural Networks for Static Inverse Optimization and Characteristics of Data Interpretation
Hong ZHANG, Masumi ISHIKAWA,
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Abstract(in English) In this paper we propose structures of neural networks corresponding to various forms of criterion functions and constraints for solving static inverse optimization problems. In interpreting real data, we compare two kinds of constraints, i.e linear constraints and quadratic ones. In computer simulation we either generate linear constraints or a quadratic constraint from artificial data, and examine marginal rates of substitution at Perato optimal data under either linear constraints or a quadratic constraint.
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Keyword(in English) structure of neural network / learning / static optimization / constraints
Paper # NC2002-38
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Committee NC
Conference Date 2002/7/19(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) Structures of Neural Networks for Static Inverse Optimization and Characteristics of Data Interpretation
Sub Title (in English)
Keyword(1) structure of neural network
Keyword(2) learning
Keyword(3) static optimization
Keyword(4) constraints
1st Author's Name Hong ZHANG
1st Author's Affiliation Graduate School of Life Science & Systems Engineering Kyushu Institute of Technology()
2nd Author's Name Masumi ISHIKAWA
2nd Author's Affiliation Graduate School of Life Science & Systems Engineering Kyushu Institute of Technology
Date 2002/7/19
Paper # NC2002-38
Volume (vol) vol.102
Number (no) 253
Page pp.pp.-
#Pages 6
Date of Issue