Abstract
Modeling nonlinear systems in terms of fuzzy rules often encounters a few problems such as the conflict between overfitting and underfitting, and low reliability that increases the number of the necessary fuzzy rules. To deal with these problems, we propose a hybrid fuzzy-neural modeling technique. Performance of the proposed approach is compared to that of the conventional approach for the case of forecasting the time series. Result shows that the pro-posed method is more efficient and accurate in terms of the number of fuzzy rules and its generalization.
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Kim, D., Seo, SJ., Park, GT. (2004). Hybrid Fuzzy-Neural Architecture and Its Application to Time Series Modeling. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2004. Lecture Notes in Computer Science(), vol 3215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30134-9_81
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DOI: https://doi.org/10.1007/978-3-540-30134-9_81
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