A fuzzy neural network approximator with fast terminal sliding mode and its applications

Yu, S, Yu, X and Man, Z 2004, 'A fuzzy neural network approximator with fast terminal sliding mode and its applications', Fuzzy Sets and Systems, vol. 148, no. 3, pp. 469-486.


Document type: Journal Article
Collection: Journal Articles

Title A fuzzy neural network approximator with fast terminal sliding mode and its applications
Author(s) Yu, S
Yu, X
Man, Z
Year 2004
Journal name Fuzzy Sets and Systems
Volume number 148
Issue number 3
Start page 469
End page 486
Total pages 18
Publisher Elsevier BV
Abstract This paper presents a new training method for fuzzy neural network (FNN) systems to approximate unknown nonlinear continuous functions. Fast terminal sliding mode combining the finite time convergent property of terminal attractor and exponential convergent property of linear system has faster convergence to the origin in finite time. The proposed training algorithm uses the principle of the fast terminal sliding mode into the conventional gradient descent learning algorithm. The Lyapunov stability analysis in this paper guarantees that the approximation is stable and converges to the optimal approximation function with improved speed instead of finite time convergence to unknown function. The proposed FNN approximator is then applied in the control of an unstable nonlinear system and the Duffing system. The simulation results demonstrate the effectiveness of the proposed method.
Subject Artificial Intelligence and Image Processing not elsewhere classified
Keyword(s) Approximation
Finite time convergence
Fuzzy neural network
Gradient descent learning
Terminal sliding mode
DOI - identifier 10.1016/j.fss.2003.12.004
Copyright notice © 2003 Elsevier B.V. All rights reserved.
ISSN 0165-0114
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