ESTIMATION OF CATALYTIC PERFORMANCE BY USING NEURAL NETWORKS
| Project/Area Number |
10650776
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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 |
触媒・化学プロセス
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| Research Institution | AICHI INSTITUTE OF TECHNOLOGY |
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Principal Investigator |
KITO Shigeharu AICHI INSTITUTE OF TECHNOLOGY, FACULTY OF ENG., PROFESSOR, 工学部, 教授 (20023343)
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| Co-Investigator(Kenkyū-buntansha) |
KITO Shigeharu AICHI INSTITUTE OF TECHNOLOGY, FACULTY OF ENG., PROFESSOR, 工学部, 教授 (20023343)
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| Project Period (FY) |
1998 – 1999
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| Project Status |
Completed (Fiscal Year 1999)
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| Budget Amount *help |
¥3,400,000 (Direct Cost: ¥3,400,000)
Fiscal Year 1999: ¥1,700,000 (Direct Cost: ¥1,700,000)
Fiscal Year 1998: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Keywords | neural network / catalytic performance / training data / back propagation / function overshooting / 触媒性能予測 / RBF / 正規分布関数 |
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| Research Abstract |
In this investigation, the methods of promoting estimation reliability of neural network for the case of catalytic performance are experimentally discussed from the information science viewpoints. The problem of function value overshooting encountered in the process of learning with error back-propagation neural networks is computationally investigated for the specific case of two-variable nonlinear functions. The experimental results suggests that the allowance of as large as magnitude of calculated function values should be reserved between those values and the outputs from output-layer units in order to make that learning as complete as possible. It is also concluded that the difference in application manners of training data to input layer is not critical for learning performance of those neural networks.
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Report
(3 results)
Research Products
(5 results)
