| Project/Area Number |
05555273
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| Research Category |
Grant-in-Aid for Developmental Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Research Field |
海洋工学
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| Research Institution | University of Osaka prefecture |
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Principal Investigator |
KISHI Mitsuo College of Eng., University of Osaka Prefecture Assistant Prof., 工学部, 助教授 (00145814)
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| Co-Investigator(Kenkyū-buntansha) |
YAMADA Tomoki College of Eng., Assistant, 工学部, 助手 (90240027)
HOSODA Ryusuke College of Eng., Professor, 工学部, 教授 (30081392)
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| Project Period (FY) |
1993 – 1994
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| Project Status |
Completed (Fiscal Year 1994)
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| Budget Amount *help |
¥3,000,000 (Direct Cost: ¥3,000,000)
Fiscal Year 1994: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 1993: ¥2,500,000 (Direct Cost: ¥2,500,000)
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| Keywords | Neural networks / Combinatorial optimization / Discrete optimization / Structural optimization / Distributed representation / Neuro-optimizer / Mapping technique / Gradient dynamical system / 組合せ最適化 |
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| Research Abstract |
In the middle 1980's Hopfield showed that some discrete optimization problem can be programd and solved on artificial neural networks minimizing the quadratic energy function. The basic concept of the Hopfield neural networks is a combination of the input-output neuron model and the steepest descent method.
Based on the concept of the Hopfield neural networks we propose an optimization method named NEURO-OPTIMIZER which is expected to be able to attain good solutions for general nonlinear discrete optimization problems. The NEURO-OPTIMIZER needs some algorithm to escape from local minima of the energy function. The simulated annealing method is introduced here.
The discrete variable is represented numerically by neurons in the NEURO-OPTIMIZER.The neuron state takes binary values of one or zero. In this study the number representation for irregularly discrete variable is investigated by a stability analysis, and a mapping technique is proposed as a redundant representation for irregularly discrete variables. Numerical examples for structural optimization are provided to illustrate the applicability of the NEURO-OPTIMIZER.
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