Abstract
This paper presents the proposed architecture for Ensembles of ANFIS (adaptive Network based fuzzy inference system), with emphasis on its application to prediction of chaotic time series (like the Mackey-Glass), where the goal is to minimize the prediction error. The methods used for the integration of the Ensembles of ANFIS are: Integrator by average and the integrator of weighted average. The performance obtained with the Ensemble architecture overcomes several standard statistical approaches and neural network models reported in the literature by various researchers. In the experiments we changed the type of membership functions and the desired error, thereby increasing the complexity of the training.
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Soto, J., Castillo, O., Soria, J. (2010). Chaotic Time Series Prediction Using Ensembles of ANFIS. In: Castillo, O., Kacprzyk, J., Pedrycz, W. (eds) Soft Computing for Intelligent Control and Mobile Robotics. Studies in Computational Intelligence, vol 318. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15534-5_18
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DOI: https://doi.org/10.1007/978-3-642-15534-5_18
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