Synthesis of Intelligent Learning Control Systems by Evolutionary Neural Networks
| Project/Area Number |
09450160
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | Osaka Prefecture University |
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Principal Investigator |
OMATU Sigeru Osaka Prefecture University, Department of Computer and Systems Sciences, professor, 工学部, 教授 (30035662)
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| Co-Investigator(Kenkyū-buntansha) |
YOSHIOKA Michifumi Osaka Prefecture University, Department of Computer and Systems Sciences, profes, 工学部, 講師 (70285302)
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| Project Period (FY) |
1997 – 1998
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| Project Status |
Completed (Fiscal Year 1998)
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| Budget Amount *help |
¥6,400,000 (Direct Cost: ¥6,400,000)
Fiscal Year 1998: ¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 1997: ¥3,300,000 (Direct Cost: ¥3,300,000)
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| Keywords | Neural networks / Learning ability / Evolutional programming / Genetic algorithm / Learning control / Control applications / 階層型ニューラルネットワーク / 汎化能力 / インテリジェント制御 |
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| Research Abstract |
In this project, we have proposed a new approach to realize the intelligent control systems based on neural networks. The neural networks have several specific properties over conventional information processing such as parallel processing, distributed memory, learning, etc. The present project is to utilize these properties for synthesis of intelligent control systems as well as evolution computation such as genetic algorithms and evolution programming and then apply them to real plant control problem to show the effectiveness of the proposed methods. To complete the project study, we have adopted the following approach to synthesize these intelligent control systems :
(1) Learning Ability of the Neural network
We have formulated the learning ability of neural networks by using information measure of the communication channel.
(2) Emergence of Evolution Mechanism by Genetic Algorithms
To improve the learning ability of neural networks considered (1), we have introduced the genetic algorithms and find the global minimum of the learning curves.
(3) Construction of Image Understanding by Neural Networks
To realize the intelligence we have used not only numerical data but also image information processing as a multi-media data processing.
(4) Rule Acquisition System Based on Image Information and Action Behaviors and their Applications
Under the unknown environment we have realized the intelligent control systems using the image information and action behaviors. These approaches have been applied to real control problems such as stabilization of inverted pendulum and temperature control of a heating furnace. From these experimental results the proposed approach in this study are good control results and could be applied to the other control problems.
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Report
(3 results)
Research Products
(29 results)
