Structure identification of fuzzy model
Fuzzy Sets and Systems
FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition
Information Sciences: an International Journal - Special issue on advanced neuro-fuzzy techniques and their applications
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
Parameter Selection in Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Using adaptive neuro-fuzzy inference system for hydrological time series prediction
Applied Soft Computing
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Incremental learning of dynamic fuzzy neural networks for accurate system modeling
Fuzzy Sets and Systems
Time-series forecasting using flexible neural tree model
Information Sciences: an International Journal
Handling multiple objectives with particle swarm optimization
IEEE Transactions on Evolutionary Computation
A hybrid of genetic algorithm and particle swarm optimization for recurrent network design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Time series forecasting with a hybrid clustering scheme and pattern recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A new approach to fuzzy modeling
IEEE Transactions on Fuzzy Systems
Subsethood-product fuzzy neural inference system (SuPFuNIS)
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
Computers and Electronics in Agriculture
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Time series forecasting is an important and widely interesting topic in the research of system modeling. We propose a new computational intelligence approach to the problem of time series forecasting, using a neuro-fuzzy system (NFS) with auto-regressive integrated moving average (ARIMA) models and a novel hybrid learning method. The proposed intelligent system is denoted as the NFS-ARIMA model, which is used as an adaptive nonlinear predictor to the forecasting problem. For the NFS-ARIMA, the focus is on the design of fuzzy If-Then rules, where ARIMA models are embedded in the consequent parts of If-Then rules. For the hybrid learning method, the well-known particle swarm optimization (PSO) algorithm and the recursive least-squares estimator (RLSE) are combined together in a hybrid way so that they can update the free parameters of NFS-ARIMA efficiently. The PSO is used to update the If-part parameters of the proposed predictor, and the RLSE is used to adapt the Then-part parameters. With the hybrid PSO-RLSE learning method, the NFS-ARIMA predictor may converge in fast learning pace with admirable performance. Three examples are used to test the proposed approach for forecasting ability. The results by the proposed approach are compared to other approaches. The performance comparison shows that the proposed approach performs appreciably better than the compared approaches. Through the experimental results, the proposed approach has shown excellent prediction performance.