Implementation of Back Propagation Algorithm Using MATLAB
Source: Kurukshetra University Artificial Neural Network (ANN)
are highly interconnected and highly parallel systems. Back
Propagation is a common method of training artificial neural
networks so as to minimize objective function. This paper
describes the implementation of back propagation algorithm. The
error generated at the output is fed back to the input and
weights of the neurons are updated by supervised learning and it
is a generalization of delta rule. The sigmoid function is used
as a activation function. The design is simulated using MATLAB
R2008a version. Maximum accuracy has been achieved.