A Fast Hybrid Algorithm of Global Optimization for Feedforward Neural Networks*
نویسندگان
چکیده
This paper presents the hybrid algorithm of global optimization of dynamic learning rate for multilayer feedforward neural networks (MLFNN). The effect of inexact line search on conjugacy was studied and a generalized conjugate gradient method based on this effect was proposed and shown to have global convergence for error backpagation of MLFNN. The descent property and global convergence was given for the improved hybrid algrithm of conjugate gradient algorithm, the results of the proposed algorithm show a considerable improvement over the FletcherRreeves algorithm and conventional BP algorithm, it overcomes the drawback of conventional BP and Polak-Ribieve conjugate gradient algorithm that maybe plung into local minima.
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