Enhancing the Performance of Neurofuzzy Predictors by Emotional Learning Algorith

Neural networks and Neurofuzzy models have been successfully used in the prediction of nonlinear time series. Several learning methods have been introduced to train the Neurofuzzy predictors, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. However, they differ in computational complexity. The emotional Learning, which is successfully used in bounded rational decision making, is introduced as an appropriate method to achieve particular goals in the prediction of real world data. For example, predicting the peaks of sunspot numbers (maximum of solar activity) is more important due to its major effects on earth and satellites. The emotional learning based fuzzy inference system (ELFIS) has the advantages of simplicity and low computational complexity in comparison with other multi-objective optimization methods. The efficiency of proposed predictor is shown in two examples of highly nonlinear time series. Appropriate emotional signal is composed for the prediction of solar activity and price of securities. It is observed that ELFIS performs better predictions in the important regions of solar maximum, and is also a fast and efficient algorithm to enhance the performance of ANFIS predictor in both examples.