Type-2 fuzzy neural network using grey wolf optimizer learning algorithm for nonlinear system identification

2017 ◽  
Vol 24 (10) ◽  
pp. 4075-4088 ◽  
Author(s):  
Wei-Lung Mao ◽  
Suprapto ◽  
Chung-Wen Hung
Keyword(s):  
Neural Network ◽  
System Identification ◽  
Nonlinear System ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Nonlinear System Identification ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Fuzzy Neural

System Identification Based on Dynamical Training for Recurrent Interval Type-2 Fuzzy Neural Network

2013 ◽  
pp. 188-205
Author(s):  
Tsung-Chih Lin ◽  
Yi-Ming Chang ◽  
Tun-Yuan Lee
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
System Identification ◽  
Nonlinear System ◽  
Fuzzy Neural Networks ◽  
Training Data ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Recurrent Interval

This paper proposes a novel fuzzy modeling approach for identification of dynamic systems. A fuzzy model, recurrent interval type-2 fuzzy neural network (RIT2FNN), is constructed by using a recurrent neural network which recurrent weights, mean and standard deviation of the membership functions are updated. The complete back propagation (BP) algorithm tuning equations used to tune the antecedent and consequent parameters for the interval type-2 fuzzy neural networks (IT2FNNs) are developed to handle the training data corrupted by noise or rule uncertainties for nonlinear system identification involving external disturbances. Only by using the current inputs and most recent outputs of the input layers, the system can be completely identified based on RIT2FNNs. In order to show that the interval IT2FNNs can handle the measurement uncertainties, training data are corrupted by white Gaussian noise with signal-to-noise ratio (SNR) 20 dB. Simulation results are obtained for the identification of nonlinear system, which yield more improved performance than those using recurrent type-1 fuzzy neural networks (RT1FNNs).

A NOVEL EFFICIENT LEARNING ALGORITHM FOR SELF-GENERATING FUZZY NEURAL NETWORK WITH APPLICATIONS

2012 ◽  
Vol 22 (01) ◽  
pp. 21-35 ◽  
Author(s):  
FAN LIU ◽  
MENG JOO ER
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Time Series Prediction ◽  
Nonlinear System Identification ◽  
Prediction Problem ◽  
Compact Structure ◽  
Wide Range ◽  
Fuzzy Neural ◽  
Efficient Learning

In this paper, a novel efficient learning algorithm towards self-generating fuzzy neural network (SGFNN) is proposed based on ellipsoidal basis function (EBF) and is functionally equivalent to a Takagi-Sugeno-Kang (TSK) fuzzy system. The proposed algorithm is simple and efficient and is able to generate a fuzzy neural network with high accuracy and compact structure. The structure learning algorithm of the proposed SGFNN combines criteria of fuzzy-rule generation with a pruning technology. The Kalman filter (KF) algorithm is used to adjust the consequent parameters of the SGFNN. The SGFNN is employed in a wide range of applications ranging from function approximation and nonlinear system identification to chaotic time-series prediction problem and real-world fuel consumption prediction problem. Simulation results and comparative studies with other algorithms demonstrate that a more compact architecture with high performance can be obtained by the proposed algorithm. In particular, this paper presents an adaptive modeling and control scheme for drug delivery system based on the proposed SGFNN. Simulation study demonstrates the ability of the proposed approach for estimating the drug's effect and regulating blood pressure at a prescribed level.

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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