A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Compensatory Genetic Fuzzy Neural Networks and Their Applications
Compensatory Genetic Fuzzy Neural Networks and Their Applications
A fuzzy neural network approach to machine condition monitoring
Computers and Industrial Engineering - Special issue: Selected papers from the 25th international conference on computers & industrial engineering in New Orleans, Louisiana
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
An ART-based fuzzy adaptive learning control network
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive
IEEE Transactions on Fuzzy Systems
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Compensatory neurofuzzy systems with fast learning algorithms
IEEE Transactions on Neural Networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
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By utilizing some of the important properties of wavelets like denoising, compression, multiresolution along with the concepts of fuzzy logic and neural network, a new self-constructing fuzzy wavelet neural networks (SCFWNN) using compensatory fuzzy operators are proposed for intelligent fault diagnosis. An on-line learning algorithm is applied to automatically construct the SCFWNN. There are no rules initially in the SCFWNN. They are created and adapted as on-line learning proceeds via simultaneous structure and parameter learning. The advantages of this learning algorithm are that it converges quickly and the obtained fuzzy rules are more precise. The proposed SCFWNN is much more powerful than either the neural network or the fuzzy system since it can incorporate the advantages of both. The results of simulation show that this SCFWNN method has the advantage of faster learning rate and higher diagnosing precision.