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| Presentation | 2012-05-26 13:30 Revised GMDH-type neural network algorithm using prediction error criterion defined as AIC Tadashi Kondo, Junji Ueno, Shoichiro Takao (Tokushima Univ.)  KBSE2012-9 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this study, a revised Group Method of Data Handling (GMDH)-type neural network algorithm using prediction error criterion defined as AIC, is proposed. In this algorithm, the optimum neural network architecture is automatically selected from three types of neural network architectures such as sigmoid function type neural network, radial basis function (RBF) type neural network and polynomial type neural network. Furthermore, the structural parameters such as the number of feedback loops, the number of neurons in the hidden layers and useful input variables are automatically selected so as to minimize the prediction error criterion defined as Akaike’s Information Criterion (AIC). Feedback GMDH-type neural network has a feedback loop and the complexity of the neural network increases gradually using feedback loop calculations so as to fit the complexity of the nonlinear system. This algorithm is applied to identification problem of the complex nonlinear system. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | GMDH / Neural network / Nonlinear system identification / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 64, KBSE2012-9, pp. 49-54, May 2012. | |
| Paper # | KBSE2012-9 | |
| Date of Issue | 2012-05-18 (KBSE) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | KBSE2012-9 |
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| Conference Information | |
| Committee | KBSE |
| Conference Date | 2012-05-25 - 2012-05-26 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Keio University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | General session and Student session |
| Paper Information | |
| Registration To | KBSE |
| Conference Code | 2012-05-KBSE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Revised GMDH-type neural network algorithm using prediction error criterion defined as AIC |
| Sub Title (in English) | |
| Keyword(1) | GMDH |
| Keyword(2) | Neural network |
| Keyword(3) | Nonlinear system identification |
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| 1st Author's Name | Tadashi Kondo |
| 1st Author's Affiliation | The University of Tokushima (Tokushima Univ.) |
| 2nd Author's Name | Junji Ueno |
| 2nd Author's Affiliation | The University of Tokushima (Tokushima Univ.) |
| 3rd Author's Name | Shoichiro Takao |
| 3rd Author's Affiliation | The University of Tokushima (Tokushima Univ.) |
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| Speaker | Author-1 |
| Date Time | 2012-05-26 13:30:00 |
| Presentation Time | 45 minutes |
| Registration for | KBSE |
| Paper # | KBSE2012-9 |
| Volume (vol) | vol.112 |
| Number (no) | no.64 |
| Page | pp.49-54 |
| #Pages | 6 |
| Date of Issue | 2012-05-18 (KBSE) |
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