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Volume 15,Issue 1, Jan. - Feb. 2024
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Training Feed Forward Neural Networks Using Optimized Back Propagation Algorithm
V. Sowjanya; J. Sirisha; T. Kranthi kumar
Neural network (NN) is one of the most
important data mining techniques. It is used with both
supervised and unsupervised learning. Training NN is a
complex task of great importance in problems of supervised
learning. Most of NN training algorithms make use of
gradient-based search. These methods have the advantage of
the directed search, in that weights are always updated in
such a way that minimizes the error, which called NN
learning process. However, there are several negative aspects
with these algorithm such as dependency to a learning rate
parameter, network paralysis, slowing down by an order of
magnitude for every extra (hidden) layer added and complex
and multi-modal error space. The back-propagation
algorithm is one of the most famous algorithm to train a feed
forward network. Instead of its success rate the quest for
development is observed through the various standard
modifications in-order to meet the challenges of complex
applications. This paper proposes an optimized back
propagation algorithm to train feed forward artificial neural
networks.