Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks
Summary :
In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E86-A No.9 pp.2309-2316
- Publication Date
- 2003/09/01
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Section on Nonlinear Theory and its Applications)
- Category
- Optimization and Control
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Cheng-Jian LIN, Cheng-Hung CHEN, "Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E86-A, no. 9, pp. 2309-2316, September 2003, doi: .
Abstract: In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/e86-a_9_2309/_p
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@ARTICLE{e86-a_9_2309,
author={Cheng-Jian LIN, Cheng-Hung CHEN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks},
year={2003},
volume={E86-A},
number={9},
pages={2309-2316},
abstract={In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.},
keywords={},
doi={},
ISSN={},
month={September},}
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TY - JOUR
TI - Nonlinear System Control Using Compensatory Neuro-Fuzzy Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 2309
EP - 2316
AU - Cheng-Jian LIN
AU - Cheng-Hung CHEN
PY - 2003
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E86-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2003
AB - In this paper, a Compensatory Neuro-Fuzzy Network (CNFN) for nonlinear system control is proposed. The compensatory fuzzy reasoning method is using adaptive fuzzy operations of neural fuzzy network that can make the fuzzy logic system more adaptive and effective. An on-line learning algorithm is proposed to automatically construct the CNFN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The structure learning is based on the fuzzy similarity measure and the parameter learning is based on backpropagation algorithm. The advantages of the proposed learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The performance of CNFN compares excellently with other various exiting model.
ER -
English
