| Project/Area Number |
13650486
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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 |
Control engineering
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| Research Institution | KYOTO INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
KUROE Yasuaki KYOTO INSTITUTE OF TECHNOLOGY, DEPARTMENT OF ENGINEERING AND DESIGN, PROFESSOR, 工芸学部, 教授 (10153397)
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| Co-Investigator(Kenkyū-buntansha) |
MORI Takehiro KYOTO INSTITUTE OF TECHNOLOGY, DEPARTMENT OF ENGINEERING AND DESIGN, PROFESSOR, 工芸学部, 教授 (60026359)
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥1,200,000 (Direct Cost: ¥1,200,000)
Fiscal Year 2003: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2002: ¥600,000 (Direct Cost: ¥600,000)
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| Keywords | neural network / learning method / information fusion / recurrent neural network / associative memory / model inclusion leraning / complex neural network / robot vision / 学習 / 形状復元問題 / ニューロオシレータ / 写像能力 |
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| Research Abstract |
Brains of human beings are the most superior information processing machine in existence. They fuse various information obtained with five sensors and recognize the external world. The objective of this research project is to develop mathematical model of information fusion performed by biological systems and develop methods for realize information fusion by artificial neural networks. We propose learning methods of artificial neural networks, which are capable of fusing various informations. We also investigate applications of the information-fusion learning of neural networks. The results are summarized as follows.
It is very difficult to deal with the problem of information fusion in general framework. Firstly, for simplicity, we considered the case that internal relations among different kinds of informations are available as teaching data. Taking account of internal relations among different kinds of informations we proposed learning methods which realize not only input-output of a
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target system but also the internal relations of various kinds of date obtained from the system. We developed the information-fusion learning algorithms for both multilayer networks and recurrent neural networks. Similar investigations were made for complex valued neural networks, which can directly deal with complex numbers (signals).
Secondly, in order to deal with the case that internal relations among different kinds of informations are not available, we extend the learning methods. We developed a framework of neural network learning which includes the model of a target system in the learning loop.
Furthermore we investigated applications of the information-fusion learning methods this developed to several engineering problems such as problem of shape from shading and estimation of motion fields in computer vision, estimation of flow velocity fields, realizations of associative memories, synthesis of neural oscillators and so on. It has been shown that in several applications the information-fusion learning methods successfully improve their performance. Less
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