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Adali, T., Bakal, B., Sönmez, M. K., Fakory, R., and Tsaoi, C. O. (1997).
Modeling nuclear reactor core dynamics with recurrent neural networks.
Neurocomputing, 15(3-4):363-381.

Alon, N., Dewdney, A. K., and Ott, T. J. (1991).
Efficient simulation of finite automata by neural nets.
Journal of the Association of Computing Machinery, 38(2):495-514.

Alquézar, R. and Sanfeliu, A. (1995).
An algebraic framework to represent finite state automata in single-layer recurrent neural networks.
Neural Computation, 7(5):931-949.

Aussem, A., Murtagh, F., and Sarazin, M. (1995).
Dynamical recurrent neural networks -- towards environmental time series prediction.
International Journal of Neural Systems, 6:145-170.

Baltersee, J. and Chambers, J. (1997).
Non-linear adaptive prediction of speech with a pipelined recurrent neural network and a linearised recursive least squares algorithm.
In Proceedings of ECSAP'97, European Conference on Signal Analysis & Prediction.

Bengio, Y., Simard, P., and Frasconi, P. (1994).
Learning long-term dependencies with gradient descent is difficult.
IEEE Transactions on Neural Networks, 5(2):157-166.

Blair, A. and Pollack, J. B. (1997).
Analysis of dynamical recognizers.
Neural Computation, 9(5):1127-1142.

Box, G. E. P., Jenkins, G. M., and Reinsel, G. C. (1994).
Time series analysis: forecasting and control.
Prentice-Hall, Englewood Cliffs, NJ.
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Bradley, M. J. and Mars, P. (1995).
Application of recurrent neural networks to communication channel equalization.
In IEEE International Conference on Acoustics, Speech and Signal Processing, volume 5, pages 3399-3402.

Bridle, J. S. (1990).
Alphanets: A recurrent neural network architecture with a hidden Markov model interpretation.
Speech Communication, 9:83-92.

Bullock, T. H. and Horridge, A. G. (1965).
Structure and Function in The Nervous System of Invertebrates.
W.H. Freeman and Co., New York, NY.

Bulsari, A. B. and Saxén, H. (1995).
A recurrent network for modeling noisy temporal sequences.
Neurocomputing, 7(1):29-40.

Burks, A. W. and Wang, H. (1957).
The logic of automata.
Journal of the ACM, 4:193-218 and 279-297.

Carrasco, R. C., Forcada, M. L., and Santamaría, L. (1996).
Inferring stochastic regular grammars with recurrent neural networks.
In Miclet, L. and de la Higuera, C., editors, Grammatical Inference: Learning Syntax from Sentences, pages 274-281, Berlin. Springer-Verlag.
Proceedings of the Third International Colloquium on Grammatical Inference, Montpellier, France, 25-27 September 1996.

Carrasco, R. C., Forcada, M. L., Valdés-Muñoz, M. Á., and Ñeco, R. P. (2000).
Stable encoding of finite-state machines in discrete-time recurrent neural nets with sigmoid units.
Neural Computation, 12(9):2129-2174.

Casey, M. (1996).
The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction.
Neural Computation, 8(6):1135-1178.

Cauwenberghs, G. (1993).
A fast-stochastic error-descent algorithm for supervised learning and optimization.
In Advances in Neural Information Processing Systems 5, pages 244-251, San Mateo, CA. Morgan-Kaufmann.

Cauwenberghs, G. (1996).
An analog VLSI recurrent neural network learning a continuous-time trajectory.
IEEE Transactions on Neural Networks, 7(2):346-361.

Chalmers, D. J. (1990).
Syntactic transformations on distributed representations.
Connection Science, pages 53-62.

Chen, T.-B., Lin, K. H., and Soo, V.-W. (1997).
Training recurrent neural networks to learn lexical encoding and thematic role assignment in parsing Mandarin Chinese sentences.
Neurocomputing, 15(3):383-409.

Chen, W.-Y., Liao, Y.-F., and Chen, S.-H. (1995).
Speech recognition with hierarchical recurrent neural networks.
Pattern Recognition, 28(6):795-805.

Cheng, Y., Karjala, T. W., and Himmelblau, D. M. (1995).
Identification of nonlinear dynamic processes with unknown and variable dead time using an internal recurrent neural network.
Ind. Eng. Chem. Res., 34:1735-1742.

Chiu, C.-C. and Shanblatt, M. A. (1995).
Human-like dynamic programming neural networks for dynamic time warping speech recognition.
Int. J. Neural Syst., 6(1):79-89.

Chomsky, N. (1965).
Aspects of the Theory of Syntax.
MIT Press, Cambridge, MA.

Chovan, T., Catfolis, T., and Meert, K. (1994).
Process control using recurrent neural networks.
In 2nd IFAC Workshop on Computer Software Structures Integrating AI/KBS System in Process Control.

Chovan, T., Catfolis, T., and Meert, K. (1996).
Neural network architecture for process control based on the RTRL algorithm.
AIChE Journal, 42(2):493-502.

Chrisman, L. (1991).
Learning recursive distributed representations for holistic computation.
Connection Science, 3(4):345-366.

Cid-Sueiro, J., Artes-Rodriguez, A., and Figueiras-Vidal, A. R. (1994).
Recurrent radial basis function networks for optimal symbol-by-symbol equalization.
Signal Processing, 40:53-63.

Cid-Sueiro, J. and Figueiras-Vidal, A. R. (1993).
Recurrent radial basis function networks for optimal blind equalization.
In Neural Networks for Processing III: Proceedings of the 1993 IEEE-SP Workshop, pages 562-571.

Cleeremans, A., Servan-Schreiber, D., and McClelland, J. L. (1989).
Finite state automata and simple recurrent networks.
Neural Computation, 1(3):372-381.

Clouse, D., Giles, C., Horne, B., and Cottrell, G. (1994).
Learning large debruijn automata with feed-forward neural networks.
Technical Report CS94-398, Computer Science and Engineering, University of California at San Diego, La Jolla, CA.

Clouse, D. S., Giles, C. L., Horne, B. G., and Cottrell, G. W. (1997a).
Representation and induction of finite state machines using time-delay neural networks.
In Mozer, M. C., Jordan, M. I., and Petsche, T., editors, Advances in Neural Information Processing Systems, volume 9, page 403. The MIT Press.

Clouse, D. S., Giles, C. L., Horne, B. G., and Cottrell, G. W. (1997b).
Time-delay neural networks: Representation and induction of finite-state machines.
IEEE Transactions on Neural Networks, 8(5):1065-1070.

Connor, J. T. and Martin, R. D. (1994).
Recurrent neural networks and robust time series prediction.
IEEE Trans. Neural Networks, 5(2):240-254.

Das, S. and Das, R. (1991).
Induction of discrete state-machine by stabilizing a continuous recurrent network using clustering.
Computer Science and Informatics, 21(2):35-40.
Special Issue on Neural Computing.

Das, S. and Mozer, M. (1994).
A unified gradient-descent/clustering architecture for finite state machine induction.
In Cowan, J., Tesauro, G., and Alspector, J., editors, Advances in Neural Information Processing Systems 6, pages 19-26. San Mateo, CA: Morgan Kaufmann.

Das, S. and Mozer, M. (1998).
Dynamic on-line clustering and state extraction: an approach to symbolic learning.
Neural Networks, 11(1):53-64.

Dertouzos, M. (1965).
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MIT Press, Cambridge, MA.

Draye, J., Pavisic, D., Cheron, G., and Libert, G. (1995).
Adaptive time constants improve the prediction capability of recurrent neural networks.
Neural Processing Letters, 2(3):12-16.

Dreider, J. F., Claridge, D. E., Curtiss, P., Dodier, R., Haberl, J. S., and Krarti, M. (1995).
Building energy use prediction and system identification using recurrent neural networks.
Journal of Solar Energy Engineering, 117:161-166.

Elman, J. (1991).
Distributed representations, simple recurrent networks, and grammatical structure.
Machine Learning, 7(2/3):195-226.

Elman, J. L. (1990).
Finding structure in time.
Cognitive Science, 14:179-211.

Fahlman, S. E. (1991).
The recurrent cascade-correlation architecture.
In Lippmann, R. P., Moody, J. E., and Touretzky, D. S., editors, Advances in Neural Information Processing Systems 3, pages 190-196. Morgan Kaufmann, Denver, CO.

Forcada, M. L. and Carrasco, R. C. (1995).
Learning the initial state of a second-order recurrent neural network during regular-language inference.
Neural Computation, 7(5):923-930.

Forcada, M. L. and Carrasco, R. C. (2001).
Simple stable encodings of finite-state machines in dynamic recurrent networks, pages 103-127.
IEEE Press.

Forcada, M. L. and Ñeco, R. P. (1997).
Recursive hetero-associative memories for translation.
In Mira, J., Moreno-Díaz, R., and Cabestany, J., editors, Biological and Artificial Computation: From Neuroscience to Technology (Proceedings of the 1997 International Work-conference on Artificial and Natural Neural Networks), volume 1240 of Lecture Notes in Computer Science, pages 453-462, Berlin. Springer-Verlag.

Frasconi, P., Gori, M., Maggini, M., and Soda, G. (1996).
Representation of finite-state automata in recurrent radial basis function networks.
Machine Learning, 23:5-32.

Gilbert, E. N. (1954).
Lattice theoretic properties of frontal switching functions.
Journal of Math. and Physics, 33:57-67.

Giles, C., Sun, G., Chen, H., Lee, Y., and Chen, D. (1990).
Higher order recurrent networks & grammatical inference.
In Touretzky, D., editor, Advances in Neural Information Processing Systems 2, pages 380-387, San Mateo, CA. Morgan Kaufmann.

Giles, C. L., Chen, D., Sun, G. Z., Chen, H. H., Lee, Y. C., and Goudreau, M. W. (1995).
Constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution.
IEEE Transactions on Neural Networks, 6(4):829-836.

Giles, C. L., Miller, C. B., Chen, D., Chen, H. H., Sun, G. Z., and Lee, Y. C. (1992).
Learning and extracted finite state automata with second-order recurrent neural networks.
Neural Computation, 4(3):393-405.

Gori, M., Bengio, Y., and De Mori, R. (1989).
BPS: A learning algorithm for capturing the dynamical nature of speech.
In Proceedings of the IEEE-IJCNN89, Washington.

Gori, M., Maggini, M., Martinelli, E., and Soda, G. (1998).
Inductive inference from noisy examples using the hybrid finite state filter.
IEEE Transactions on Neural Networks, 9(3):571-575.

Goudreau, M., Giles, C., Chakradhar, S., and Chen, D. (1994).
First-order vs. second-order single layer recurrent neural networks.
IEEE Transactions on Neural Networks, 5(3):511-513.

Haykin, S. (1998).
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Prentice-Hall, Upper Saddle River, NJ.

Haykin, S. and Li, L. (1995).
Nonlinear adaptive prediction of nonstationary signals.
IEEE Transactions on Signal Processing, 43(2):526-535.

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Bounds on the complexity of recurrent neural network implementations of finite state machines.
Neural Networks, 9(2):243-252.

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Ellis Horwood, New York, NY.

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Using recurrent neural networks for adaptive communication channel equalization.
IEEE Transactions on Neural Networks, 5(2):267-278.

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On the computational power of Elman-style recurrent networks.
IEEE Transactions on Neural Networks, 6(4):1000-1004.

Kremer, S. C. (1996a).
Comments on ``constructive learning of recurrent neural networks: limitations of recurrent cascade correlation and a simple solution''.
IEEE Transactions on Neural Networks, 7(4):1047-1051.
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Kremer, S. C. (1996b).
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Kremer, S. C. (1999).
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IEEE Transactions on Neural Networks, 10(2):433-438.

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IEEE Transactions on Neural Networks, 7(6):1329-1338.

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