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Fuzzy Sets and Systems
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Accurate on-line support vector regression
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Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling
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IEEE Transactions on Computers
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Fuzzy basis functions, universal approximation, and orthogonal least-squares learning
IEEE Transactions on Neural Networks
A fuzzy neural network with fuzzy impact grades
Neurocomputing
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
Complex fuzzy computing to time series prediction: a multi-swarm PSO learning approach
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting
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On the inference and approximation properties of belief rule based systems
Information Sciences: an International Journal
Information Sciences: an International Journal
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In this paper we propose a novel incremental learning approach based on a hybrid fuzzy neural net framework. A key feature of the approach is the adaptation of the fuzzy neural network (FNN) modeling to every new data. The typical algorithm of FNN is inefficient when used in an accurate online time series because they must be retrained from scratch every time the training set is modified. In order to reduce the expense of FNN learning for a dynamic system, a general methodology leading to quick algorithms for FNN modeling is developed. The FNN-LM algorithm for a static FNN and incremental learning algorithm (ILA) for dynamic fuzzy neural network (DFNN) are also presented to enforce the model to approximate every new sample. The ILA approach has the advantages of avoiding increasing the ranks of matrixes and avoiding solving the inverse matrix when samples increase gradually. When it is used to predict an accurate online time series, the DFNN model can efficiently update a trained static FNN with a very fast speed according to the sample added to the training set. Numerical experiments validate our theoretical results. Excellent performances of the proposed approach in modeling accuracy and learning convergence are exhibited.