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A Hybrid System of Hidden Markov Models and Recurrent Neural Networks for Learning Deterministic Finite State Automata

Authors: Pavan K. Rallabandi, Kailash C. Patidar


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.

Keywords: Hybrid systems, Hidden Markov Models, deterministic finite state automata, Recurrent neural networks

Digital Object Identifier (DOI):

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[1] Y.S. Abu-Mostafa, Learning from hints in neural networks, Journal of Complexity (1990) 6:192.
[2] B. Pandey and R.B. Mishra, Knowledge and intelligent computing system in medicine, Computers in Biology and Medicine, 39(3):215-230, 2009.
[3] E. Barnard and D. Casasent, Invariance and neural networks, IEEE Transactions on Neural Networks, 2 :498-508, 1991.
[4] Y. Bengio, Neural Networks for Speech and Sequence Recognition, International Thompson Computer Press, 1996.
[5] C.M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
[6] K. David, D. Haussler, G. Martin Reese and H. Frank Eeckman, A generalized hidden Markov model for the recognition of human genes in DNA, Procedings of the Fourth International Conference on Intelligent Systems for Molecular Biology, AAAI Press, 1996.
[7] Richard O. Duda, Peter E. Hart, and David G. Stork. 2000. Pattern Classification (2nd Edition). Wiley-Interscience.
[8] J.L. Elman and D. Zipser, Learning the hidden structure of speech, Journal of the Acoustical Society of America, 83:1615-1626, 1988.
[9] L. Fu, Mapping rule-based systems into neural architecture, Knowledge Based Systems, 3(1):48-56, 1990.
[10] Y. Fukuoka and H. Matsuki, A Modified Back-propagation Method to Avoid Local Minima, Neural Networks, 11:1059-1072, 1998.
[11] M.J.F. Gales, Maximum likelihood linear transformations for Hidden Markov Model-based speech recognition, Computer Speech Language, 12:75-98, 1998.
[12] A.S. Weigend and N.A. Gershenfeld, The Future of Time Series, Learning and Understanding, Addison-Wesley, Reading, MA, 1-17, 1993.
[13] C.L. Giles, D. Chen, C.B. Miller, H.H. Chen, G.Z. Sun and Y.C. Lee, Second-order recurrent neural networks for grammatical inference, 1991 IEEE INNS International Joint Conference on Neural Networks, Piscataway, NJ, IEEE Press. Reprinted in Artificial Neural Networks, eds. E. Sanchez-Sinencia, C. Lau, IEEE Press, 273-281, 1992.
[14] Y. Hayashi and A. Imura, Fuzzy neural expert systems with automated extraction of fuzzy if-then rules from a trained neural network, Procedings of First IEEE Conference on Fuzzy Systems (1990) 489-494.
[15] J. Hertz, A. Krogh and R.G. Palmer, Introduction to the Theory of Neural Computation. Addison-Wesley Publishing Company, 1991.
[16] J.J. Hopfield, Neural networks and physical systems with emergent collective computational facilities, Proceedings of the National Academy of Sciences of the USA, 79:2554–2558, 1982.
[17] K. Hornik, M. Stinchcombe and H. White, Multilayer feedforward networks are universal approximators, Neural Networks, 2:359-366, 1989.
[18] J.F. Kolen and S.C. Kremer, (ed.), A Field Guide to Dynamical Recurrent Networks, IEEE Press, Piscataway, NJ, 2001.
[19] S. Lawrence, C. L. Giles and A.C. Tsoi, Symbolic conversion, grammatical inference and rule extraction of foreign exchange rate prediction, Decision Technologies for Financial Engineering: Procedings of the Fourth International Conference on Neural Networks in the Capital Markets, World Scientific, Singapore (1998) 333-345.
[20] K. Marakami and H Taguchi, Gesture recognition using recurrent neural networks, Procedings of the SIGCHI conference on Human factors in computing systems, 237-242, 1991.
[21] L. R. Rabiner, A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition , Proceedings of the IEEE, 77(2):257-285, 1989.
[22] R.D. Reed and J. Robert Mark, Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, The MIT Press, 1999.
[23] A.J Robinson, An application of recurrent neural nets to phone probability estimation, IEEE transactions on Neural Networks, 5(2):298-305, 1994.
[24] D.E. Rumelhart, J.L. McCLelland and the PDP Research Group, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Foundations, MIT Press, Cambridge, MA, 1, 1986.
[25] G.G. Towell and J.W. Shavlik, The extraction of refined rules from knowledge-based neural networks, Machine Learning, 13(1) (1993) 71–101.
[26] G.G. Towell and J.W. Shavlik. Knowledge-based artificial neural networks. Artificial Intelligence, 70(1-2):119-165, 1994.
[27] P.J. Werbos, Backpropagation through time; what it does and how to do it, Proceedings of the IEEE, 78:1550-1560, 1990.
[28] R.J. Williams and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, 1(2):270-280, 1989.
[29] R.J. Williams and D. Zipser, Gradient-based learning algorithms for recurrent networks and their computational complexity, In Back-propagation: Theory, Architectures and Applications, 433-486. 1995.