Multilayer feedforward networks are universal approximators
Neural Networks
Ten lectures on wavelets
An introduction to wavelets
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
System identification (2nd ed.): theory for the user
System identification (2nd ed.): theory for the user
An Adaptive Learning Algorithm for a Neuro-fuzzy Network
Proceedings of the International Conference, 7th Fuzzy Days on Computational Intelligence, Theory and Applications
Prediction of chaotic time series using neural network
NN'09 Proceedings of the 10th WSEAS international conference on Neural networks
Computational Intelligence in Optimization: Applications and Implementations
Computational Intelligence in Optimization: Applications and Implementations
Adaptive fuzzy wavelet neural controller design for chaos synchronization
Expert Systems with Applications: An International Journal
Intelligent target recognition based on wavelet adaptive network based fuzzy inference system
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
A globally convergent adaptive predictor
Automatica (Journal of IFAC)
IEEE Transactions on Fuzzy Systems
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
A recurrent self-organizing neural fuzzy inference network
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
A new class of wavelet networks for nonlinear system identification
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
| Hi-index | 0.07 |
In the paper a five-layers architecture of hybrid wavelet-neuro-fuzzy system which is using the adaptive W-neurons as the nodes is proposed. W-neuron is a neuron which structure is similar to a radial basis functions network, but instead of conventional radial basis functions we used multidimensional adaptive wavelet activation-membership functions. The distinctive feature of the proposed system is usage of the wavelets as membership functions in the antecedent layer, and the adaptive multidimensional wavelets as activation functions in the consequent layer that can tune not only the dilation and translation parameters but also its own form during the learning process. The learning algorithms for all antecedent and consequent functions' parameters that have both following and filtering properties are proposed. The experimental results have shown that this wavelet-neuro-fuzzy system has improved approximation properties and has a higher learning rate in comparison with usual wavelet-neuro-fuzzy networks. The proposed hybrid wavelet-neuro-fuzzy system can be used to solve tasks of diagnosis, forecasting, emulation, and identification of nonlinear chaotic and stochastic non-stationary processes.