  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.
