Adaptive Critic Learning Algorithm of Neuro-Fuzzy Inference System
Date Issued
2006
Date
2006
Author(s)
Tu, Chia-Hsiang
DOI
en-US
Abstract
The goal of this research is to develop an adaptive critic neuro-fuzzy inference system (NFIS) for modeling and control. On the backbone of dual heuristic programming (DHP), a DHP adaptive critic learning scheme that utilizes an effective network Jacobian acquisition is proposed. In control applications, the adaptive critic NFIS can learn from scratch to achieve the control objective. In modeling applications, it can approximate arbitrary continuous function through sequential optimization. The learning structure is based on NFIS that contains fuzzy if-then rules of first-order Sugeno fuzzy model. The tuning rules of premise and consequent parameters are derived. Narendra’s benchmark system is used to verify the performance of the proposed adaptive critic learning algorithm. The ability of modeling is demonstrated by approximating a nonlinear continuous function. The proposed design is applied to obtain the control of a rotary inverted pendulum control. Simulation results show that the rotary pendulum system can learn from scratch to obtain swing-up, balancing and trajectory tracking control.
Subjects
模糊類神經網路
自評自調
雙啟發規劃法
類神經網路
模糊控制
Neuro-fuzzy inference system
adaptive critic
dual heuristic programming
neural network
fuzzy control
Type
thesis
File(s)![Thumbnail Image]()
Loading...
Name
ntu-95-R93921079-1.pdf
Size
23.31 KB
Format
Adobe PDF
Checksum
(MD5):f50643df20c6d8fa80825973fa883bb6