Abstract
In this paper, we propose a neuro-fuzzy modeling framework to discover fuzzy rules and its application to predict chemical properties of ashes produced by thermo-electric generators. The framework is defined by several sequential steps in order to obtain a good predictive accuracy and the readability of the discovered fuzzy rules. First, a feature selection procedure is applied to the available data by discarding the features possessing lowest ranking in terms of their predictive power. Then, a competitive learning scheme is adopted to initialize a fuzzy rule base, which is successively refined by a neuro-fuzzy network trained on the available data. To improve accuracy, we applied the process on each ash property to be predicted, hence obtaining a set of MISO models that are both accurate and transparent, as shown by the reported experimental results.
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© 2003 Springer-Verlag Berlin Heidelberg
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Castellano, G., Castiello, C., Fanelli, A.M., Mencar, C. (2003). Discovering Prediction Rules by a Neuro-fuzzy Modeling Framework. In: Palade, V., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2003. Lecture Notes in Computer Science(), vol 2773. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45224-9_168
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DOI: https://doi.org/10.1007/978-3-540-45224-9_168
Publisher Name: Springer, Berlin, Heidelberg
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