Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Ten lectures on wavelets
Local feedback multilayered networks
Neural Computation
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling
Fuzzy Sets and Systems
Modeling and prediction with a class of time delay dynamic neural networks
Applied Soft Computing
Adaptive mixtures of local experts
Neural Computation
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
Artificial wavelet neural network and its application in neuro-fuzzy models
Applied Soft Computing
Nonlinear systems control using self-constructing wavelet networks
Applied Soft Computing
Wavelet neural networks for function learning
IEEE Transactions on Signal Processing
Identification of nonlinear dynamic systems using functional linkartificial neural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent fuzzy-neural model for dynamic system identification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification and control of dynamic systems using recurrent fuzzy neural networks
IEEE Transactions on Fuzzy Systems
Structure identification of generalized adaptive neuro-fuzzy inference systems
IEEE Transactions on Fuzzy Systems
Computational capabilities of local-feedback recurrent networks acting as finite-state machines
IEEE Transactions on Neural Networks
Using wavelet network in nonparametric estimation
IEEE Transactions on Neural Networks
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Hybrid adaptive wavelet-neuro-fuzzy system for chaotic time series identification
Information Sciences: an International Journal
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In this paper different structure of the neurons in the hidden layer of a feed-forward network, for forecasting of the dynamic systems, are proposed. Each neuron in the network is a combination of the sigmoidal activation function (SAF) and wavelet activation function (WAF). The output of the hidden neuron is the product of the output from these two activation functions. A delay element is used to feedback the output of the sigmoidal and the wavelet activation function to each other. This arrangement leads to proposed five possible configurations of recurrent neurons. Besides proposing these neuron models, the presented paper tries to compare the performance of wavelet function with sigmoid function. To guarantee the stability and the convergence of the learning process, upper bound for the learning rates has been investigated using the Lyapunov stability theorem. A two-phase adaptive learning rate ensures this upper bound. Universal approximation property of the feed-forward network with the proposed neurons has also been investigated. Finally, the applicability and comparison of the proposed recurrent networks has been weathered on two benchmark problem catering different types of dynamical systems.