1998 Fiscal Year Final Research Report Summary
Computational Study on the Structure and Learning in the Integrated Neural Networks for Different Sensors
| Project/Area Number |
09680365
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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 |
Intelligent informatics
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| Research Institution | Nagoya Institute of Technology |
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Principal Investigator |
ISHII Naohiro Nagoya Inst.of Faculty of Engineering, Professor., 工学部, 教授 (50004619)
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| Co-Investigator(Kenkyū-buntansha) |
YAMAUCHI Koichiro Nagoya Inst.of Tech., Faculty of Engineering, , Assistant Professor., 工学部, 助手 (00262949)
IWAHORI Yuugi Nagoya Inst.of Technology, Faculty of Engineering, , Associate Professor., 工学部, 助教授 (60203402)
IKEDA Tetsuo Nagoya Inst.of Technology, Faculty of Engineering, Professor., 工学部, 教授 (50005253)
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| Project Period (FY) |
1997 – 1998
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| Keywords | Neural network / Asymmetric network / Structure of network / Function of network / Nonlinear analysis / Integrated network |
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| Research Abstract |
Biological neural networks play important roles in the sensory infor- mation processing, which is different from artificial neural networks. The retinal neural networks in the visual system, are classified into symmetrical and asymmetrical structures. The asymmetrical networks, which are shown in catfish and cat et al., consist of the linear pathway and the nonlinear pathway. This asymmetrical networks have characteristic properties in the sensory perceptions.
The asymmetric structures reflect the function of the neural networks. Thus, this study, first analyzes what is the functions of the asymmetric neural networks in the sensory perception. Second, the integrated neural netwoks are discussed to respond and learn different sensory informations from different perceptual networks. The integrated network consist of several sub-netwoks to recognize objects correctly by using several sensors."Forward Network" receives inputs from corresponding sensor. "Integrating Unit" integrates outputs of all. Forward Networks."Backward Network" receives inputs from Integrating Unit and recostructs the sensory information as its outputs. In the recognition phase, the integ- rated system gets a correct output by modifying the inputs of Forward Networks, the output of Integrating Unit and the confidence of the sensory information repeatedly to maximize a likelihood function , which can be derived by a Bayesian method. It was clarified the system has the improved ability in the integrated networks.
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