Şenol, CananYıldırım, Tülay2019-06-282019-06-28201011755-05561755-0556https://hdl.handle.net/20.500.12469/1581https://doi.org/10.1504/IJRIS.2010.036873In this paper a novel fuzzy-neural network architecture is proposed and the algorithm is developed. Using this new architecture fuzzy-CSFNN fuzzy-MLP and fuzzy-RBF configurations were constituted and their performances have been compared on medical diagnosis problems. Here conic section function neural network (CSFNN) is also a hybrid neural network structure that unifies the propagation rules of multilayer perceptron (MLP) and radial basis function (RBF) neural networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own self-network structure. The proposed hybrid fuzzy-neural networks were implemented in a well-known benchmark medical problems with real clinical data for thyroid disorders breast cancer and diabetes disease diagnosis. Simulation results show that proposed hybrid structures outperform both MATLAB-ANFIS and non-hybrid structures. © 2010 Inderscience Enterprises Ltd.eninfo:eu-repo/semantics/closedAccessfuzzy neural networksfuzzy-CSFNNfuzzy-MLPfuzzy-neural hybrid schemesfuzzy-RBFmedical diagnosisArticle265271210.1504/IJRIS.2010.0368732-s2.0-84952972764N/AQ4