engleski

An Efficient Newton-type learning Algorithm for MLP Neural Networks

Multilayer perceptrons (MLP) are the most often used neural networks in function approximation applications. They learn by modifying the strength of interconnections between neurons, according to some specified rule called learning algorithm. Many different learning algorithms have been reported in the literature. The majority of them are based on gradient numerical optimization methods such as the steepest descent, conjugate gradient, quasi-Newton and Newton methods. In this paper we have proposed a new Newton-type learning algorithm which is a modification of the popular Levenberg-Marquardt learning algorithm. The algorithm has been compared with the original Levenberg-Marquardt algorithm regarding the convergence speed and the computation complexity on four nonlinear test functions. Also the effects of the data sets size and extremely high accuracy requirements on the efficiency of the algorithms have been analyzed. To provide the algorithms comparison as objective as possible, both algorithms were implemented on the same manner and the network weights were initialized equally for both of them. The proposed algorithm exhibited better performances in all test cases.

Newton-type learning Algorithm; MLP Neural Networks

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