In recent years, singular systems and fuzzy descriptor have attracted a lot of researchers' attention due to their wide practical applications for modeling complex phenomena. In this study, one approach proposed is the fuzzy clustering algorithm based on linear structures to identify the neuro Fuzzy local linear models. Additionally fuzzy descriptor models, a recently proposed neuro fuzzy interpretation of locally linear models, are implemented because of their promise for intuitive incremental learning algorithms e.g. Generalized Fuzzy Clustering Variety (GFCV). The results from the fuzzy descriptor models are compared to the results of several other methods. An efficient technique, based on the error indices of multiple validation sets, is used to optimize the number of neurons and prevent the algorithm from over fitting. The scope of this work is to reveal the advantages of fuzzy descriptor models and compare them to the most successful neural and neuro fuzzy approaches based on prediction accuracy, generalization, and computational complexity. The proposed solution is shown to accurately forecast seismic time series, outperforming several other methods.
['B. Bahrami', 'Masoud Shafiee']
Fuzzy Descriptor Models for Earthquake Time Prediction Using Seismic Time Series