| Presentation | 2015-12-19 Limited General Regression Neural Network for embedded systems and its implementation method to increase its throughput Daisuke Nishio, Koichiro Yamauchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Limited general regression neural network (LGRNN) / incremental learning / learning on a budget / embedded systems / response time / Real time OS (RTOS) |
| Paper # | NC2015-46 |
| Date of Issue | 2015-12-12 (NC) |
| Conference Information | |
| Committee | MBE / NC |
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| Conference Date | 2015/12/19(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nagoya Institute of Technology |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Tetsuo Kobayashi(Kyoto Univ.) / Toshimichi Saito(Hosei Univ.) |
| Vice Chair | Yutaka Fukuoka(Kogakuin Univ.) / Shigeo Sato(Tohoku Univ.) |
| Secretary | Yutaka Fukuoka(akita noken) / Shigeo Sato(Kogakuin Univ.) |
| Assistant | Takenori Oida(Kyoto Univ.) / Ryota Horie(Shibaura Inst. of Tech.) / Hiroyuki Kanbara(Tokyo Inst. of Tech.) / Hisanao Akima(Tohoku Univ.) |
| Paper Information | |
| Registration To | Technical Committee on ME and Bio Cybernetics / Technical Committee on Neurocomputing |
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| Language | JPN |
| 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) |
| 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.) |
| Date | 2015-12-19 |
| Paper # | NC2015-46 |
| Volume (vol) | vol.115 |
| Number (no) | NC-384 |
| Page | pp.pp.1-6(NC), |
| #Pages | 6 |
| Date of Issue | 2015-12-12 (NC) |