| Project/Area Number |
16500129
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Osaka Kyoiku University |
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Principal Investigator |
BABA Norio Osaka Kyoiku University, Faculty of Education, Professor, 教育学部, 教授 (30035654)
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| Project Period (FY) |
2004 – 2005
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| Project Status |
Completed (Fiscal Year 2005)
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| Budget Amount *help |
¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2005: ¥500,000 (Direct Cost: ¥500,000)
Fiscal Year 2004: ¥700,000 (Direct Cost: ¥700,000)
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| Keywords | Soft Computing / Learning Automata / Hierarchical Structure / Nonstationary Environment / Prediction of Stock Price / Computer Gaming / Fusion of Soft Computing / Neural Networks / 学習オートマン / 非定常環境 / 学習 / 適応 / 学習オートマトン |
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| Research Abstract |
Despite the current matured state concerning the theory and applications of the soft computing techniques, there are still several problems to be settled. One of the most important problems might be : "How to cope with the nonstationary environment ?" Recently, the following idea hit me : "Fusion of learning automata and soft computing techniques may contribute a lot in finding a successful way for solving the above problem."
In this research project, I was mainly involved in the research to investigate the learning performance of the hierarchical structure learning automata under the unknown nonstationary multiteacher environment in order to check whether this idea is OK. I was also involved to the research which deals with the real problems that contain rather strong nonlinearity.
The following are our research results :
1.We proposed a new learning algorithm for the hierarchical structure learning automata operating in the nonstationary multiteacher environment. We proved that the proposed algorithm ensures convergence with probability 1 to the optimal path under a certain nonstationary multiteacher environment.
2.In order to compare the learning performance of the proposed algorithm in the nonstationary multiteacher environment with the algorithms DGPA and SE_<RI> (two of the fastest algorithms today), we carried out a rather large numbers of the computer simulations. The simulation results we obtained confirm the effectiveness of the proposed algorithm.
3.We have applied neural networks and genetic algorithms to the fields of financial engineering & computer gaming. We succeeded in obtaining the following results :
(1)Neural networks are quite helpful in order to predict the Golden Cross and the Dead Cross several weeks before they occur.
(2)Fusion of neural networks and genetic algorithms is also quite helpful for finding an appropriate rule to make the Environmental Game much more exciting.
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