Hardware Implementation of Neural Networks with Learning Capability Using Simultaneous Perturbation
| Project/Area Number |
19500198
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | Kansai University |
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Principal Investigator |
MAEDA Yutaka Kansai University, システム理工学部, 教授 (60209393)
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| Project Period (FY) |
2007 – 2009
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| Project Status |
Completed (Fiscal Year 2009)
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| Budget Amount *help |
¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
Fiscal Year 2009: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2008: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2007: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
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| Keywords | ニューラルネットワーク / 同時摂動最適化法 / ハードウェア実現 / FPGA / 学習機能 / サポートベクターマシン / FPAA / パルスニューロン / サポートベルターマシン / バルスニューロン |
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| Research Abstract |
In this research, we demonstrated feasibility of a learning rule using the simultaneous perturbation optimization method for artificial neural networks. First, we showed that combination of pulse density expression and the simultaneous perturbation method is useful for hardware implementation of neural networks. Second, we fabricated support vector machine with learning mechanism using the simultaneous perturbation method based on FPGA. Finally, a pulse coupled oscillator with learning capability is realized as an analog circuit system using FPAA and the simultaneous perturbation method.
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Report
(4 results)
Research Products
(37 results)
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[Presentation] 同時摂動を用いたPSO2008
Author(s)
前田裕, 松下直人
Organizer
第52回システム制御情報学会研究発表講演会
Place of Presentation
京都情報大学院大学
Year and Date
2008-05-16
Related Report
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