| 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)
|
| 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 |
|---|
| 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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