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
A new cluster validity index for the fuzzy c-mean
Pattern Recognition Letters
Neuro-fuzzy systems for function approximation
Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
A distance-based fuzzy time series model for exchange rates forecasting
Expert Systems with Applications: An International Journal
Adaptive Filtering Prediction and Control
Adaptive Filtering Prediction and Control
Self-organizing neuro-fuzzy system for control of unknown plants
IEEE Transactions on Fuzzy Systems
Pseudoerror-based self-organizing neuro-fuzzy system
IEEE Transactions on Fuzzy Systems
International Journal of Intelligent Systems in Accounting and Finance Management
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Exchange rate fluctuation has a significant effect on the risk of marketing business, economic development and financial stability. Accurate prediction for exchange rate may reduce commercial and economic risk arisen by exchange rate fluctuation. In this study, we propose an intelligent approach to the forecasting problem of the CNY-USD exchange rate, where a neurofuzzy self-organizing system is used as the intelligent predictor. For learning purpose, a novel hybrid learning method is devised for the intelligent predictor, where the well-known particle swarm optimization (PSO) algorithm and the recursive least squares estimator (RLSE) algorithm are involved. The proposed learning method is called the PSO-RLSE-PSO method. Experiments for time series forecasting of the CNY-USD exchange rate are conducted. For performance, the intelligent predictor is trained by several different methods. The experimental results show that the proposed approach has excellent forecasting performance.