Training fuzzy neural networks using sliding mode theory with adaptive learning rate

2012 ◽  
Author(s):  
Alireza Zarif Khoramdel Azad ◽  
Mojtaba Ahmadieh Khanesar ◽  
Mohammad Teshnehlab
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Learning Rate ◽  
Mode Theory ◽  
Adaptive Learning Rate ◽  
Fuzzy Neural

Sliding mode incremental learning algorithm for interval type-2 Takagi–Sugeno–Kang fuzzy neural networks

2012 ◽  
Vol 3 (3) ◽  
pp. 179-188 ◽  
Author(s):  
Sevil Ahmed ◽  
Nikola Shakev ◽  
Andon Topalov ◽  
Kostadin Shiev ◽  
Okyay Kaynak
Keyword(s):  
Neural Networks ◽  
Incremental Learning ◽  
Sliding Mode ◽  
Learning Algorithm ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Interval Type ◽  
Takagi Sugeno

CONSTRUCTIVE APPROACHES FOR TRAINING OF WAVELET NEURAL NETWORKS USING ADAPTIVE LEARNING RATE

2013 ◽  
Vol 11 (03) ◽  
pp. 1350021
Author(s):  
MOHAMED ZINE EL ABIDINE SKHIRI ◽  
MOHAMED CHTOUROU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Fixed Number ◽  
Stability Theorem ◽  
Learning Rate ◽  
Training Algorithms ◽  
Lyapunov Stability Theorem ◽  
Constructive Approach ◽  
Wavelet Neural Networks ◽  
Adaptive Learning Rate

This paper investigates the applicability of the constructive approach proposed in Ref. 1 to wavelet neural networks (WNN). In fact, two incremental training algorithms will be presented. The first one, known as one pattern at a time (OPAT) approach, is the WNN version of the method applied in Ref. 1. The second approach however proposes a modified version of Ref. 1, known as one epoch at a time (OEAT) approach. In the OPAT approach, the input patterns are trained incrementally one by one until all patterns are presented. If the algorithm gets stuck in a local minimum and could not escape after a fixed number of successive attempts, then a new wavelet called also wavelon, will be recruited. In the OEAT approach however, all the input patterns are presented one epoch at a time. During one epoch, each pattern is trained only once until all patterns are trained. If the resulting overall error is reduced, then all the patterns will be retrained for one more epoch. Otherwise, a new wavelon will be recruited. To guarantee the convergence of the trained networks, an adaptive learning rate has been introduced using the discrete Lyapunov stability theorem.

Intelligent dynamic sliding-mode neural control using recurrent perturbation fuzzy neural networks

2016 ◽  
Vol 173 ◽  
pp. 734-743 ◽  
Author(s):  
Chun-Fei Hsu ◽  
Chun-Wei Chang
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Fuzzy Neural ◽  
Dynamic Sliding Mode

Optimization and Research of the Adaptive Wiener Filter Based on Fuzzy Neural Networks

2013 ◽  
Vol 411-414 ◽  
pp. 1660-1664
Author(s):  
Yan Jun Zhao ◽  
Li LIU
Keyword(s):  
Neural Networks ◽  
Adaptive Learning ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Wiener Filter ◽  
Fuzzy Neural Networks ◽  
Central Value ◽  
Fuzzy Neural ◽  
Training Error ◽  
Neural Network Technology

This paper introduces fuzzy neural network technology into the adaptive filter and makes further research on its structure and algorithms. At first, fuzzy rules are determined and the network structure is built by means of dividing fuzzy subspaces. Secondly, membership functions are chosen layers are defined and the network is trained by adaptive learning algorithm. Thirdly, training error is the minimum with repeating debugging. Finally, linking weight, the central value and width of the network membership function is adjusted by using experience of experts. The optimal performance of Adaptive Wiener Filter is realized based on Fuzzy Neural Networks.

scholarly journals Improved Adaptive Sliding Mode Control for a Class of Uncertain Nonlinear Systems Subjected to Input Nonlinearity via Fuzzy Neural Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Tat-Bao-Thien Nguyen ◽  
Teh-Lu Liao ◽  
Jun-Juh Yan
Keyword(s):  
Neural Networks ◽  
Adaptive Control ◽  
Sliding Mode Control ◽  
Sliding Mode ◽  
Fuzzy Neural Networks ◽  
Adaptive Sliding Mode Control ◽  
Input Nonlinearity ◽  
Mode Control ◽  
Model Dynamics ◽  
Fuzzy Neural

The paper presents an improved adaptive sliding mode control method based on fuzzy neural networks for a class of nonlinear systems subjected to input nonlinearity with unknown model dynamics. The control scheme consists of the modified adaptive and the compensation controllers. The modified adaptive controller online approximates the unknown model dynamics and input nonlinearity and then constructs the sliding mode control law, while the compensation controller takes into account the approximation errors and keeps the system robust. Based on Lyapunov stability theorem, the proposed method can guarantee the asymptotic convergence to zero of the tracking error and provide the robust stability for the closed-loop system. In addition, due to the modification in controller design, the singularity problem that usually appears in indirect adaptive control techniques based on fuzzy/neural approximations is completely eliminated. Finally, the simulation results performed on an inverted pendulum system demonstrate the advanced functions and feasibility of the proposed adaptive control approach.

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