A Neurofuzzy-Based Adaptive Predictor for Control of Nonlinear Systems
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- HU Jinglu
- Graduate School of Information Science and Electrical Engineering, Kyushu University
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- HIRASAWA Kotaro
- Graduate School of Information Science and Electrical Engineering, Kyushu University
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- KUMAMARU Kousuke
- Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology
Bibliographic Information
- Other Title
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- Neurofuzzy-Based Adaptive Predictor for Control of Nonlinear Systems
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Abstract
This paper proposes an adaptive predictor for general nonlinear systems based on the use of a class of neurofuzzy models. The neurofuzzy-based predictor can be interpreted as a linear predictor network consisting of a global linear predictor and several local linear predictors with interpolation. It has some distinctive features as well as good prediction ability: its parameters have explicit meanings useful for initial value setting in parameter adjustment; it may be transformed into a form linear for the variables synthesized in control systems, which makes deriving a control law straightforward. Simulations on applying it to adaptive control of nonlinear systems demonstrate its usefulness.
Journal
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- Transactions of the Society of Instrument and Control Engineers
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Transactions of the Society of Instrument and Control Engineers 35 (8), 1060-1068, 1999
The Society of Instrument and Control Engineers
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Keywords
Details
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- CRID
- 1390001204502163328
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- NII Article ID
- 10004576692
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- NII Book ID
- AN00072392
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- ISSN
- 18838189
- 04534654
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- NDL BIB ID
- 4834798
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- Text Lang
- en
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- Data Source
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- JaLC
- NDL
- Crossref
- CiNii Articles
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- Abstract License Flag
- Disallowed