System identification: theory for the user
System identification: theory for the user
Forecasting enrollments based on fuzzy time series
Fuzzy Sets and Systems
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
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Predicting Chaotic Time Series Using Neural and Neurofuzzy Models: A Comparative Study
Neural Processing Letters
A neural network ensemble method with jittered training data for time series forecasting
Information Sciences: an International Journal
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Operation properties and δ-equalities of complex fuzzy sets
International Journal of Approximate Reasoning
Complex neuro-fuzzy self-learning approach to function approximation
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
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
Complex fuzzy model with PSO-RLSE hybrid learning approach to function approximation
International Journal of Intelligent Information and Database Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
ANCFIS: A Neurofuzzy Architecture Employing Complex Fuzzy Sets
IEEE Transactions on Fuzzy Systems
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A self-organizing complex neuro-fuzzy intelligent approach using complex fuzzy sets (CFSs) is presented in this paper for the problem of time series forecasting. CFS is an advanced fuzzy set whose membership function is characterized within a unit disc of the complex plane. With CFSs, the proposed complex neuro-fuzzy system (CNFS) that acts as a predictor has excellent adaptive ability. The design for the proposed predictor comprises the structure and parameter learning stages. For structure learning, the FCM-Based Splitting Algorithm for clustering was used to determine an appropriate number of fuzzy rules for the predictor. For parameter learning, we devised a learning method that integrates the method of particle swarm optimization and the recursive least squares estimator in a hybrid and cooperative way to optimize the predictor for accurate forecasting. Four examples were used to test the proposed approach whose performance was then compared to other approaches. The experimental results indicate that the proposed approach has shown very good performance and accurate forecasting.