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| Presentation | 2011-03-07 11:30 Maximum Power Point Tracking Converter Using a Limited General Regression Neural Network Koichiro Yamauchi (Chubu Univ)  NC2010-134 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we propose a limited general regression neural network (LGRNN) for embedded systems. The LGRNN is an improved version of general regression neural network that continues incremental learning under a fixed number of hidden units. Initially, the LGRNN learns new samples incrementally by allocating new hidden units. If the number of hidden units reaches the upper bound, the LGRNN has to remove one useless hidden unit to learn a new sample. However, there are cases in which the adverse effects of removing a useless unit are greater than the positive effects of learning the new sample. In this case, the LGRNN should refrain from learning the new sample. To achieve this, the LGRNN predicts the effects of several learning options (e.g., ignore or learning) before the learning process begins, and chooses the best learning option to be executed. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | imited General Regression Neural Networks / Kernel Machine / pproximated Linear Dependency / Incremental Learning / Maximum Power Point Tracking (MPPT) / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 110, no. 461, NC2010-134, pp. 41-46, March 2011. | |
| Paper # | NC2010-134 | |
| Date of Issue | 2011-02-28 (NC) | |
| 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 | NC2010-134 |
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| Conference Information | |
| Committee | NC MBE |
| Conference Date | 2011-03-07 - 2011-03-09 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Tamagawa University |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2011-03-NC-MBE |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Maximum Power Point Tracking Converter Using a Limited General Regression Neural Network |
| Sub Title (in English) | |
| Keyword(1) | imited General Regression Neural Networks |
| Keyword(2) | Kernel Machine |
| Keyword(3) | pproximated Linear Dependency |
| Keyword(4) | Incremental Learning |
| Keyword(5) | Maximum Power Point Tracking (MPPT) |
| Keyword(6) | |
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| 1st Author's Name | Koichiro Yamauchi |
| 1st Author's Affiliation | Chubu University (Chubu Univ) |
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| Speaker | Author-1 |
| Date Time | 2011-03-07 11:30:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2010-134 |
| Volume (vol) | vol.110 |
| Number (no) | no.461 |
| Page | pp.41-46 |
| #Pages | 6 |
| Date of Issue | 2011-02-28 (NC) |
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