Journal ArticleDOI
Fuzzy wavelet networks for function learning
TLDR
Inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts, a fuzzy wavelet network (FWN) is proposed for approximating arbitrary nonlinear functions.Abstract:
Inspired by the theory of multiresolution analysis (MRA) of wavelet transforms and fuzzy concepts, a fuzzy wavelet network (FWN) is proposed for approximating arbitrary nonlinear functions. The FWN consists of a set of fuzzy rules. Each rule corresponding to a sub-wavelet neural network (WNN) consists of single-scaling wavelets. Through efficient bases selection, the dimension of the approximated function does not cause the bottleneck for constructing FWN. Especially, by learning the translation parameters of the wavelets and adjusting the shape of membership functions, the model accuracy and the generalization capability of the FWN can be remarkably improved. Furthermore, an algorithm for constructing and training the fuzzy wavelet networks is proposed. Simulation examples are also given to illustrate the effectiveness of the method.read more
Citations
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A survey of online failure prediction methods
TL;DR: To capture the wide spectrum of approaches concerning this area, a taxonomy has been developed, whose different approaches are explained and major concepts are described in detail.
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Prediction of compressive strength of concrete containing fly ash using artificial neural networks and fuzzy logic
TL;DR: Training and testing results have shown that artificial neural networks and fuzzy logic systems have strong potential for predicting 7, 28 and 90 days compressive strength of concretes containing fly ash.
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Adaptive Fuzzy Control of a Class of Nonlinear Systems by Fuzzy Approximation Approach
TL;DR: A variable separation approach is developed to overcome the difficulty from the nonstrict-feedback structure and a state feedback adaptive fuzzy tracking controller is proposed, which guarantees that all of the signals in the closed-loop system are bounded, while the tracking error converges to a small neighborhood of the origin.
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
TL;DR: 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.
Journal ArticleDOI
Brief paper: Novel adaptive neural control design for nonlinear MIMO time-delay systems
TL;DR: A novel adaptive neural controller is obtained by constructing a novel quadratic-type Lyapunov-Krasovskii functional, which not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients.
References
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TL;DR: It is demonstrated that neural networks can be used effectively for the identification and control of nonlinear dynamical systems and the models introduced are practically feasible.
Journal ArticleDOI
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
Li-Xin Wang,Jerry M. Mendel +1 more
TL;DR: Using the Stone-Weierstrass theorem, it is proved that linear combinations of the fuzzy basis functions are capable of uniformly approximating any real continuous function on a compact set to arbitrary accuracy.
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Wavelet networks
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
Using wavelet network in nonparametric estimation
TL;DR: Algorithms for wavelet network construction are proposed for the purpose of nonparametric regression estimation and particular attentions are paid to sparse training data so that problems of large dimension can be better handled.
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.