Now showing items 1-4 of 4

  • Fuzzy-neural networks for medical diagnosis 

    Authors:Şenol, Canan; Yıldırım, Tülay
    Publisher and Date:(2010)
    In 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 ...

  • Signature verification using conic section function neural network 

    Authors:Şenol, Canan; Yıldırım, Tülay
    Publisher and Date:(2005)
    This paper presents a new approach for off-line signature verification based on a hybrid neural network (Conic Section Function Neural Network-CSFNN). Artificial Neural Networks (ANNs) have recently become a very important method for classification and verification problems. In this work CSFNN was proposed for the signature verification and compared with two well known neural network architectures (Multilayer Perceptron-MLP and Radial Basis Function-RBF Networks). The proposed system was trained ...

  • Signature verification using conic section function neural network 

    Authors:Şenol, Canan; Yıldırım, Tülay
    Publisher and Date:(Springer-Verlag Berlin, 2005)
    This paper presents a new approach for off-line signature verification based on a hybrid neural network (Conic Section Function Neural Network-CSFNN). Artificial Neural Networks (ANNs) have recently become a very important method for classification and verification problems. In this work CSFNN was proposed for the signature verification and compared with two well known neural network architectures (Multilayer Perceptron-MLP and Radial Basis Function-RBF Networks). The proposed system was trained ...

  • Thyroid and breast cancer disease diagnosis using Fuzzy-neural networks 

    Authors:Canan, Senol; Yıldırım, Tülay
    Publisher and Date:(2009)
    In this paper a new hybrid structure in which Neural Network and Fuzzy Logic are combined is proposed and its algorithm is developed. Fuzzy-CSFNN Fuzzy-MLP and Fuzzy-RBF structures are constituted and their performances are compared. Conic Section Function Neural Network (CSFNN) unifies the propagation rules of the Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks at a unique network by its distinctive propagation rules. That means CSFNNs accommodate MLPs and RBFs in its own ...