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| Presentation | 2015-12-19 13:00 Limited General Regression Neural Network for embedded systems and its implementation method to increase its throughput Daisuke Nishio, Koichiro Yamauchi (Chubu Univ.)  NC2015-46 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recent improvement of the microcomputer enables the execution of complex intelligent algorithms on embedded systems. But, in the case of using a usual incremental learning method, its resource is often increased with learning , so that it is hard to continue to execute the incremental learning on small embedded systems. One of the author has already proposed a Limited General Regression Neural Network (LGRNN) for such limited environments. %%%%%%%%%%%%%%%%%%%%%%% LGRNN continues incremental learning within a certain number of kernels with maintaining its flexibility , by replacing the most redundant kernel with a new kernel which records current new sample. In this study, we developed an implementation technique for LGRNN to reduce its response-time. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Limited general regression neural network (LGRNN) / incremental learning / learning on a budget / embedded systems / response time / Real time OS (RTOS) / / | |
| Reference Info. | IEICE Tech. Rep., vol. 115, no. 384, NC2015-46, pp. 1-6, Dec. 2015. | |
| Paper # | NC2015-46 | |
| Date of Issue | 2015-12-12 (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 | NC2015-46 |
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| Conference Information | |
| Committee | MBE NC |
| Conference Date | 2015-12-19 - 2015-12-19 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | NC |
| Conference Code | 2015-12-MBE-NC |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Limited General Regression Neural Network for embedded systems and its implementation method to increase its throughput |
| Sub Title (in English) | |
| Keyword(1) | Limited general regression neural network (LGRNN) |
| Keyword(2) | incremental learning |
| Keyword(3) | learning on a budget |
| Keyword(4) | embedded systems |
| Keyword(5) | response time |
| Keyword(6) | Real time OS (RTOS) |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Daisuke Nishio |
| 1st Author's Affiliation | Chubu University (Chubu Univ.) |
| 2nd Author's Name | Koichiro Yamauchi |
| 2nd Author's Affiliation | Chubu University (Chubu Univ.) |
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| Speaker | Author-1 |
| Date Time | 2015-12-19 13:00:00 |
| Presentation Time | 25 minutes |
| Registration for | NC |
| Paper # | NC2015-46 |
| Volume (vol) | vol.115 |
| Number (no) | no.384 |
| Page | pp.1-6 |
| #Pages | 6 |
| Date of Issue | 2015-12-12 (NC) |
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