| Presentation | 2011-03-07 Maximum Power Point Tracking Converter Using a Limited General Regression Neural Network Koichiro YAMAUCHI, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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. Moreover, we also show a practical application of this method: an adaptive maximum power point tracking (MPPT) converter using the LGRNN. The device learns the properties of photo voltaic using the LGRNN algorithm, and achieves a quick control of the step down switching converter. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Limited General Regression Neural Networks / Kernel Machine / Approximated Linear Dependency / Incremental Learning / Maximum Power Point Tracking(MPPT) |
| Paper # | NC2010-134 |
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| Committee | NC |
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| Conference Date | 2011/2/28(1days) |
| Place (in Japanese) | (See Japanese page) |
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| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Neurocomputing (NC) |
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| Language | JPN |
| 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) | Limited General Regression Neural Networks |
| Keyword(2) | Kernel Machine |
| Keyword(3) | Approximated Linear Dependency |
| Keyword(4) | Incremental Learning |
| Keyword(5) | Maximum Power Point Tracking(MPPT) |
| 1st Author's Name | Koichiro YAMAUCHI |
| 1st Author's Affiliation | Chubu University Department of Information Science() |
| Date | 2011-03-07 |
| Paper # | NC2010-134 |
| Volume (vol) | vol.110 |
| Number (no) | 461 |
| Page | pp.pp.- |
| #Pages | 6 |
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