Performance Improvement of Competitive Associative Nets via Statistical Learning Schemes and Its Engineering Applications
| Project/Area Number |
21500217
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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 | Kyushu Institute of Technology |
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Principal Investigator |
KUROGI Shuichi 九州工業大学, 大学院・工学研究院, 教授 (40178124)
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| Project Period (FY) |
2009 – 2011
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| Project Status |
Completed (Fiscal Year 2011)
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| Budget Amount *help |
¥3,770,000 (Direct Cost: ¥2,900,000、Indirect Cost: ¥870,000)
Fiscal Year 2011: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2010: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2009: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
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| Keywords | 競合連想ネット / 統計的学習 / 制御 / 音声処理 / 距離画像処理 |
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| Research Abstract |
This research study has improved the performance of competitive associative nets called CAN2 by means of statistical learning schemes and shown its effectiveness in engineering applications. Here theCAN2 is an artificial neural net for learning efficient piecewise linear approximation of nonlinear function. We have(1) constructed and analyzed statistical learning algorithms for the CAN2,(2) applied to control of nonlinear time-varying plants,(3) applied to speech processing, and(4) applied to range image processing. The improved methods are shown to have flexibility, robustness, and effectiveness in many practical applications.
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Report
(4 results)
Research Products
(31 results)
