Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A course in fuzzy systems and control
A course in fuzzy systems and control
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Pattern recognition using neural-fuzzy networks based on improved particle swam optimization
Expert Systems with Applications: An International Journal
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Reinforcement Hybrid Evolutionary Learning for Recurrent Wavelet-Based Neurofuzzy Systems
IEEE Transactions on Fuzzy Systems
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A novel parallel hybrid intelligence optimization algorithm for a function approximation problem
Computers & Mathematics with Applications
Computers & Mathematics with Applications
Engineering Applications of Artificial Intelligence
Genetic fuzzy system for data-driven soft sensors design
Applied Soft Computing
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In this paper, an efficient method is proposed to design fuzzy wavelet neural network (FWNN) for function learning and identification by tuning fuzzy membership functions and wavelet neural networks. The structure of FWNN is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the FWNN system, an efficient genetic algorithm (GA) approach is used to adjust the parameters of dilation, translation, weights, and membership functions. By minimizing a quadratic measure of the error derived from the output of the system, the design problem can be characterized by the proposed GA formulation. Moreover, the solution is directly obtained without any need for complicated computations. The performance of our approximation is superior to that of existing methods. Several numerical design examples are likewise presented to demonstrate the design flexibility and usefulness of this presented approach.