Improvisation of Training Algorithm through Hybridization for Rule Extraction

The Artificial Neural Network is widely used for classification. In classification, training and learning of
the network in which weights and biases of the network neuron are computed to give expected output, is a
complex task. In this paper, we propose hybridization of back propagation and LevenbergMarquardt
training algorithms. The gradient derivative with respect to the weight of the network of the gradient
descent algorithm is used in augmenting Hessian matrix of Levenberg Marquardt training algorithm to
update the weight and bias of the network to converge to output. The hybrid algorithm is experimented on
two data sets. Experimental results show that it helps to achieve better network performance and extracts
fewer rules.