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Neural Networks"},{"key":"10.3233\/IDT-180346_ref84","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.fss.2015.11.018","article-title":"A comparative study of feature extraction methods and their application to P-RBF {NNs} in face recognition problem","volume":"305","author":"Oh","year":"2016","journal-title":"Fuzzy Sets and Systems"},{"key":"10.3233\/IDT-180346_ref85","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1504\/IJEF.2013.058604","article-title":"Machine learning based classifiers ensemble for credit risk assessment","volume":"7","author":"Pandey","year":"2013","journal-title":"International Journal of Electronic Finance"},{"issue":"3","key":"10.3233\/IDT-180346_ref86","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.amc.2004.06.044","article-title":"Model identification of ARIMA family using genetic algorithms","volume":"164","author":"Ong","year":"2005","journal-title":"Applied Mathematics and Computation"},{"issue":"2","key":"10.3233\/IDT-180346_ref87","doi-asserted-by":"crossref","first-page":"2664","DOI":"10.1016\/j.asoc.2010.10.015","article-title":"A novel hybridization of artificial neural networks and ARIMA models for time series forecasting","volume":"11","author":"Khashei","year":"2011","journal-title":"Applied Soft Computing Journal"},{"issue":"3","key":"10.3233\/IDT-180346_ref88","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1109\/TSMCC.2009.2038279","article-title":"Channel equalization using neural networks: A review","volume":"40","author":"Burse","year":"2010","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews)"},{"key":"10.3233\/IDT-180346_ref89","first-page":"62","article-title":"Using the multistage RBF neural network in order to predict the deposits of eghtesad novin bank and comparing this method with other methods","author":"Sharifi","year":"2014","journal-title":"J. Basic. Appl. Sci. Res"},{"key":"10.3233\/IDT-180346_ref90","doi-asserted-by":"crossref","unstructured":"Suchanek P, Marecki F, Bucki R. Self-learning Bayesian networks in diagnosis. Procedia Computer Science. 2014; 35: 1426-1435.","DOI":"10.1016\/j.procs.2014.08.200"},{"key":"10.3233\/IDT-180346_ref91","doi-asserted-by":"crossref","unstructured":"Rosipal R, Koska M, Farkas I. Prediction of chaotic time-series with a resource allocating RBF network. 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