Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A case study on predicting the wind speed

Citation:
Ismail, F. H., M. A. Aziz;, and A. E. Hassanien, "Optimizing the parameters of Sugeno based adaptive neuro fuzzy using artificial bee colony: A case study on predicting the wind speed", Federated Conference on Computer Science and Information Systems (FedCSIS),, Poland, , 11-14 Sept. , 2016.

Date Presented:

11-14 Sept.

Abstract:

This paper presents an approach based on Artificial Bee Colony (ABC) to optimize the parameters of membership functions of Sugeno based Adaptive Neuro-Fuzzy Inference System (ANFIS). The optimization is achieved by Artificial Bee Colony (ABC) for the sake of achieving minimum Root Mean Square Error of ANFIS structure. The proposed ANFIS-ABC model is used to build a system for predicting the wind speed. To ensure the accuracy of the model, a different number of membership functions has been used. The experimental results indicates that the best accuracy achieved is 98% with ten membership functions and least value of RMSE which is 0.39.

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