Browsing by Author "Chao, CT"

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A fuzzy neural network based extended Kalman filter
(1996-03-01)
Chao, CT
;
Teng, CC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
In this paper we construct a discrete extended Kalman filter by using fuzzy neural networks. The constructed filter makes it possible to estimate the states of a nonlinear dynamic system with unknown plant model. The unknown plant is identified by a fuzzy neural network that avoids the occurrence of divergence in state estimation. A computer simulation is presented to illustrate the performance and application of the proposed filter.
A PD-like self-tuning fuzzy controller without steady-state error
(1997-04-16)
Chao, CT
;
Teng, CC
;
電控工程研究所
;
Institute of Electrical and Control Engineering
This paper presents a PD-like self-tuning fuzzy controller (STFC) based on the tuning of scaling factors. A two-stage tuning method including a direct tuning stage and indirect tuning stage is proposed. For most linear controlled plants, application of the direct tuning stage alone is enough to yield satisfactory system responses. The proposed STFC can automatically detect the operating ranges of input variables and then adjust the scaling factors. In the indirect tuning stage the back-propagation algorithm can be directly applied to fine-tune the scaling factors when a more complicated plant is controlled. Furthermore, a simple but efficient method for fully compensating for steady-state error, which is the main problem in a PD-like fuzzy logic control system, is proposed so that tuning for fuzzy logic rule bases is unnecessary. Simulation results with linear time-invariant systems and a nonlinear unstable system as the controlled plants show that the proposed technique yields zero steady-state error responses very quickly without overshoot or oscillatory behavior. (C) 1997 Elsevier Science B.V.
A PD-like self-tuning fuzzy controller without steady-state error
(1997-04-16)
Chao, CT
;
Teng, CC
;
交大名義發表
;
電控工程研究所
;
National Chiao Tung University
This paper presents a PD-like self-tuning fuzzy controller (STFC) based on the tuning of scaling factors. A two-stage tuning method including a direct tuning stage and indirect tuning stage is proposed. For most linear controlled plants, application of the direct tuning stage alone is enough to yield satisfactory system responses. The proposed STFC can automatically detect the operating ranges of input variables and then adjust the scaling factors. In the indirect tuning stage the back-propagation algorithm can be directly applied to fine-tune the scaling factors when a more complicated plant is controlled. Furthermore, a simple but efficient method for fully compensating for steady-state error, which is the main problem in a PD-like fuzzy logic control system, is proposed so that tuning for fuzzy logic rule bases is unnecessary. Simulation results with linear time-invariant systems and a nonlinear unstable system as the controlled plants show that the proposed technique yields zero steady-state error responses very quickly without overshoot or oscillatory behavior. (C) 1997 Elsevier Science B.V.
Simplification of fuzzy-neural systems using similarity analysis
(1996-04-01)
Chao, CT
;
Chen, YJ
;
Teng, CC
;
交大名義發表
;
電控工程研究所
This paper presents a fuzzy neural network system (FNNS) for implementing fuzzy inference systems. In the FNNS, a fuzzy similarity measure for fuzzy rules is proposed to eliminate redundant fuzzy logical rules, so that the number of rules in the resulting fuzzy inference system will be reduced. Moreover, a fuzzy similarity measure for fuzzy sets that indicates the degree to which two fuzzy sets are equal is applied to combine similar input linguistic term nodes. Thus we obtain a method for reducing the complexity of a fuzzy neural network. We also design a new and efficient on-line initialization method for choosing the initial parameters of the FNNS. a computer simulation is presented to illustrate the performance and applicability of the proposed FNNS. The results indicates that the FNNS still has desirable performance under fewer fuzzy logical rules and adjustable parameters.