Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron
Abstract
Standard neural network based on general back propagation learning using delta method or gradient descent method has some great faults like poor optimization of error-weight objective function, low learning rate, instability .This paper introduces a hybrid supervised back propagation learning algorithm which uses trust-region method of unconstrained optimization of the error objective function by using quasi-newton method .This optimization leads to more accurate weight update system for minimizing the learning error during learning phase of multi-layer perceptron.[13][14][15] In this paper augmented line search is used for finding points which satisfies Wolfe condition. In this paper, This hybrid back propagation algorithm has strong global convergence properties & is robust & efficient in practice.
Cite
@article{arxiv.1212.1752,
title = {Hybrid Optimized Back propagation Learning Algorithm For Multi-layer Perceptron},
author = {Mriganka Chakraborty and Arka Ghosh},
journal= {arXiv preprint arXiv:1212.1752},
year = {2012}
}
Comments
Accepted for publish in 18th December, 2012,International Journal of Computer Applications, Foundation of Computer Science, New York, USA