Structure identification of fuzzy model
Fuzzy Sets and Systems
Neuro-Control Systems: Theory and Applications
Neuro-Control Systems: Theory and Applications
Control and identification of non-linear systems affected by noise using wavelet network
Second international workshop on Intelligent systems design and application
Fuzzy wavelet networks for function learning
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
The wavelet transform, time-frequency localization and signal analysis
IEEE Transactions on Information Theory
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
LMS learning algorithms: misconceptions and new results on converence
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
An adaptive recurrent fuzzy system for nonlinear identification
Applied Soft Computing
Choquet fuzzy integral based modeling of nonlinear system
Applied Soft Computing
Adaptive fuzzy wavelet network control design for nonlinear systems
Fuzzy Sets and Systems
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Fuzzy wavelet neural network models for prediction and identification of dynamical systems
IEEE Transactions on Neural Networks
An ANN embedded RTS smoother for an INS/GPS integrated Positioning and Orientation System
Applied Soft Computing
Computers & Mathematics with Applications
Hybrid-fuzzy modeling and identification
Applied Soft Computing
Choquet fuzzy integral based verification of handwritten signatures
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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, new two fuzzy wavelet neural networks (FWNNs) are proposed for approximating any arbitrary non-linear function, hence identifying a non-linear system. The output of discrete wavelet transform (DWT) block, which receives the given inputs, is fuzzified in the proposed two methods: one using compression property and other using multiresolution property. We present a new type of fuzzy neuron model, each non-linear synapse of which is characterized by a set of fuzzy implication rules with singleton weights in their consequents. It is shown that noise and disturbance in the reference signal are reduced with wavelets and also the variation of somatic gain, the parameter that controls the slope of the activation function in the neural network, leads to more accurate output. Identification results are found to be accurate and speed of their convergence is fast. Next, we simulate a control system for maintaining the output at a desired level by using the identified models. Self-learning FNN controller has been designed in this simulation. Simulation results show that the controller is adaptive and robust.