Data-Based System Modeling Using a Type-2 Fuzzy Neural Network With a Hybrid Learning Algorithm

2011 ◽  
Vol 22 (12) ◽  
pp. 2296-2309 ◽  
Author(s):  
Chi-Yuan Yeh ◽  
W. R. Jeng ◽  
Shie-Jue Lee
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
System Modeling ◽  
Hybrid Learning ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

Fuzzy Neural Network Hybrid Learning Control on AUV

2012 ◽  
Vol 468-471 ◽  
pp. 1732-1735
Author(s):  
Jing Zhao ◽  
Zhao Lin Han ◽  
Yuan Yuan Fang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Network Control ◽  
Learning Ability ◽  
B Spline ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

scholarly journals Robust Adaptive Control for Nonlinear Uncertain Systems Using Type-2 Fuzzy Neural Network System

2011 ◽  
Vol 2011 ◽  
pp. 1-25
Author(s):  
Ching-Hung Lee ◽  
Yu-Ching Lin
Keyword(s):  
Neural Network ◽  
Uncertain Systems ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Robust Adaptive Control ◽  
Control Scheme ◽  
Fuzzy Neural ◽  
Nonlinear Uncertain Systems ◽  
Robust Adaptive

This paper proposes a novel intelligent control scheme using type-2 fuzzy neural network (type-2 FNN) system. The control scheme is developed using a type-2 FNN controller and an adaptive compensator. The type-2 FNN combines the type-2 fuzzy logic system (FLS), neural network, and its learning algorithm using the optimal learning algorithm. The properties of type-1 FNN system parallel computation scheme and parameter convergence are easily extended to type-2 FNN systems. In addition, a robust adaptive control scheme which combines the adaptive type-2 FNN controller and compensated controller is proposed for nonlinear uncertain systems. Simulation results are presented to illustrate the effectiveness of our approach.

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