| Project/Area Number |
08555098
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| Research Category |
Grant-in-Aid for Scientific Research (A)
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| Allocation Type | Single-year Grants |
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| Section | 展開研究 |
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| Research Field |
計測・制御工学
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| Research Institution | The University of Tokyo |
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Principal Investigator |
HIROSE Akira Univ.Tokyo, RCAST,Assoc.Prof., 先端科学技術研究センター, 助教授 (70199115)
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| Co-Investigator(Kenkyū-buntansha) |
柴田 克成 東京大学, 先端科学技術研究センター, 助手 (10260522)
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| Project Period (FY) |
1996 – 1997
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| Project Status |
Completed (Fiscal Year 1997)
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| Budget Amount *help |
¥13,900,000 (Direct Cost: ¥13,900,000)
Fiscal Year 1997: ¥6,600,000 (Direct Cost: ¥6,600,000)
Fiscal Year 1996: ¥7,300,000 (Direct Cost: ¥7,300,000)
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| Keywords | Neural network / Parallel information processing / Neural hardware / Coherence / Optical information processing / Complex-valued neural network / Neural device / Wave-field information processing / コヒーレント型ニューラルネットワーク / 汎化特性 / バックプロパゲーション学習 / 周波数領域並列化 / リカレントネットワーク / リアプノフ指数 / カオス |
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| Research Abstract |
Brain-type information processing systems are actively studied widely, although there is no clear definition of the "brain-type systems'yet. The expected brain-type systems will be, at least, complementary to the so-called von-Neumann-type systems. In other words, the most expected feature of the future brain-type systems should be a distributed and flexible (self-organizing and/or learning) information processing based on a highly parallel architecture. That is to say, we have to realize a massively parallel and flexible hardware which has its own basis outside the Turing machine principle.
The research project aims at a construction of the prototypal subsystems of the future brain-type systems by way of realizing neural networks using wave fields such as lightwave and electromagnetic wave ; i.e., the coherent neural networks. The coherent neural networks are an expansion of the complex-valued neural networks proposed by the applicant's research group in 1992. In the present project, we have been aiming to establish the theoretical basis of the dynamic behavior of the lightwave (or electromagnetic-wave/electron-wave) coherent neural networks in which the amplitude and the phase of the information carrier are manipulated, and to realize the prototypal subsystem hardware.
In the two-year research, we have (1) elucidated the mechanism of the behavioral instability, (2) proposed and demonstrated a novel learning method to suppress the instability. We have also (3) constructed a radar system that has a complex-amplitude information processing stage by which the dynamics relation between the physics of electromagnetic wave and the complex-valued neural networks can be elucidated. The aims of this project are considered almost accomplished by these fruitful results. We expect that further theoretical and experimental research on the next stage will be carried out for constructing a more realistic coherent neural networks.
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