Investigation of Generalization Ability of a Hybrid Neural Network
نویسندگان
چکیده
In this work, generalization ability of a hybrid neural network algorithm is investigated. This algorithm consists of a combination of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) in one single network using conic section functions. The network architecture using this algorithm is called Conic Section Function Neural Network (CSFNN). Various problems are examined to demonstrate the generalization capability of CSFNN.
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