Presentation | 1999/3/19 A solution to solve dynamic inverse optimization problems using neural network learning Hong Zhang, Masumi Ishikawa, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In this paper, we propose a novel approach to solve dynamic inverse optimization problems based on neural network learning. Given input and output sequences, dynamic inverse optimization estimates a criterion function under which the given sequences are optimal for a given system to be controlled. It is to be noted that forward and inverse optimizations are represented by one and the same neural network. This makes possible the estimation of the optimal criterion function using neural network learning. To evaluate the effectiveness of the proposed method, simulation experiments are carried out by using a simple system. Dynamic inverse optimization problems are solved for both noiseless cases and noisy cases. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | dynamic inverse optimization / neural network / method of modifying eigenvalue / Riccati equation |
Paper # | NC98-166 |
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Committee | NC |
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Conference Date | 1999/3/19(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Neurocomputing (NC) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | A solution to solve dynamic inverse optimization problems using neural network learning |
Sub Title (in English) | |
Keyword(1) | dynamic inverse optimization |
Keyword(2) | neural network |
Keyword(3) | method of modifying eigenvalue |
Keyword(4) | Riccati equation |
1st Author's Name | Hong Zhang |
1st Author's Affiliation | Kyushu Institute of Technology() |
2nd Author's Name | Masumi Ishikawa |
2nd Author's Affiliation | Kyushu Institute of Technology |
Date | 1999/3/19 |
Paper # | NC98-166 |
Volume (vol) | vol.98 |
Number (no) | 674 |
Page | pp.pp.- |
#Pages | 8 |
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