Journal ArticleDOI
Fuzzy Wavelet Neural Networks for Identification and Control of Dynamic Plants—A Novel Structure and a Comparative Study
Rahib H. Abiyev,Okyay Kaynak +1 more
TLDR
The integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem of effective control of an uncertain system and results in a better performance despite its smaller parameter space.Abstract:
One of the main problems for effective control of an uncertain system is the creation of the proper knowledge base for the control system. In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rule. The parameter update rules of the system are derived based on the gradient descent method. The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is seen that the proposed structure results in a better performance despite its smaller parameter space.read more
Citations
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Journal ArticleDOI
Fuzzy Wavelet Neural Network Models for Prediction and Identification of Dynamical Systems
Sevcan Yilmaz,Yusuf Oysal +1 more
TL;DR: The proposed FWNN models are obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains.
Journal ArticleDOI
Type 2 Fuzzy Neural Structure for Identification and Control of Time-Varying Plants
Rahib H. Abiyev,Okyay Kaynak +1 more
TL;DR: It is seen that the proposed structure of a type 2 Takagi–Sugeno–Kang fuzzy neural system is a potential candidate for identification and control purposes of uncertain plants, with the uncertainties being handled adequately by type 2 fuzzy sets.
Journal ArticleDOI
A TSK-Type-Based Self-Evolving Compensatory Interval Type-2 Fuzzy Neural Network (TSCIT2FNN) and Its Applications
TL;DR: This paper proposes a Takagi-Sugeno-Kang (TSK)-type-based self-evolving compensatory interval type-2 fuzzy neural network (FNN) (TSCIT2FNN), which produces smaller root-mean-square errors and converges more quickly in system modeling and noise cancellation problems.
Journal ArticleDOI
Wavelet Fuzzy Neural Networks for Identification and Predictive Control of Dynamic Systems
TL;DR: A wavelet fuzzy neural network structure for identifying and controlling nonlinear dynamic systems and a physical positioning mechanism that is applied in numerical simulations and experiments and confirms the effectiveness of the WFNN.
Journal ArticleDOI
Hybrid ${\rm H}^{\infty}$ -Based Wavelet-Neural-Network Tracking Control for Permanent-Magnet Synchronous Motor Servo Drives
TL;DR: A hybrid H∞-based wavelet-neural-network (WNN) position tracking controller as a new robust motion-control system for permanent-magnet synchronous motor (PMSM) servo drives.
References
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Qinghua Zhang,Albert Benveniste +1 more
TL;DR: A wavelet network concept, which is based on wavelet transform theory, is proposed as an alternative to feedforward neural networks for approximating arbitrary nonlinear functions.
Journal ArticleDOI
Wavelet neural networks for function learning
TL;DR: A wavelet-based neural network is described that has universal and L/sup 2/ approximation properties and is a consistent function estimator and performed well and compared favorably to the MLP and RBF networks.
Journal ArticleDOI
Identification and control of dynamic systems using recurrent fuzzy neural networks
Ching-Hung Lee,Ching-Cheng Teng +1 more
TL;DR: The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules and is applied in several simulations (time series prediction, identification, and control of nonlinear systems).