%0 Journal Article
	%A Pavan K. Rallabandi and  Kailash C. Patidar
	%D 2015
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 107, 2015
	%T A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata
	%U https://publications.waset.org/pdf/10002715
	%V 107
	%X In this paper, we present an optimization technique or
a learning algorithm using the hybrid architecture by combining the
most popular sequence recognition models such as Recurrent Neural
Networks (RNNs) and Hidden Markov models (HMMs). In order to
improve the sequence/pattern recognition/classification performance
by applying a hybrid/neural symbolic approach, a gradient descent
learning algorithm is developed using the Real Time Recurrent
Learning of Recurrent Neural Network for processing the knowledge
represented in trained Hidden Markov Models. The developed hybrid
algorithm is implemented on automata theory as a sample test beds
and the performance of the designed algorithm is demonstrated and
evaluated on learning the deterministic finite state automata.
	%P 2317 - 2324