Revisão bibliográfica redes neurais recorrentes
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362 -SLUG: A Connectionist Architecture for Inferring the Structure of Finite-State Environments M. C. Mozer and J. Bachrach Machine Learning, Vol. 7, Number 2/3, pp. 139-160, 1991. Palavras-chave: architeture, finite state, connectionist | |
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363 -Some developmental and additional biases in the contrast enhancement and short term memory of recurrent neural networks S. Grossberg and D. Levine Journal of Theoretical Biology, Vol. 53, pp. 341-380, Academic Press, Inc., 1975. Palavras-chave: bias, memory, short term memory | |
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364 -Some Observations on the Use of the Extended Kalman Filter as a Recurrent Network Learning Algorithm R. J. Williams Technical Report, Computer Science, Northeastern University, Number NU-CCS-92-1, 1992. Palavras-chave: Kalman filter, learning, filter | |
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365 -Second-Order Recurrent Neural Networks for Grammatical Inference C. L. Giles and D. Chen and C. B. Miller and H. H. Chen and G. Z. Sun and Y. C. Lee 1991 IEEE INNS International Joint Conference on Neural Networks, Vol. II, pp. 273-281, IEEE Press, 1991. Palavras-chave: grammar, second order neural net., inference | |
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366 -Stability Properties of Labeling Recursive Auto-Associative Memory A. Sperduti IEEE Transactions on Neural Networks, 6(6), pp. 1452-1460, 1995. Palavras-chave: stability, memory, auto associative | |
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367 -Stable Encoding of Large Finite-State Automata in Recurrent Neural Networks with Sigmoid Discriminants Christian W. Omlin and C. Lee Giles Neural Computation, 8(4), pp. 675-696, 1996. Palavras-chave: automata, sigmoid discriminamts, encoding | |
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368 -Synaptic Noise in Dynamically-driven Recurrent Neural Networks: Convergence and Generalization Kam Jim and C. L. Giles and B. G. Horne Technical Report, Institute for Advanced Computer Studies, University of Maryland, Number UMIACS-TR-94-89 and CS-TR-3322, 1994. Palavras-chave: synaptic noise, dynamic system, convergence | |
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369 -Synaptic Noise in Dynamically-driven Recurrent Neural Networks: Convergence and Generalization Kam Jim and C. L. Giles and B. G. Horne IEEE Transactions on Neural Networks, 7(6), 1996. Palavras-chave: synaptic noise, dynamic system, convergence | |
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370 -Speech Dynamics and Recurrent Neural Networks H. Bourlard and C. J. Wellekens Proceedings ICASSP 89, 1989. Palavras-chave:speech dynamics | |
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371 -Training recurrent neural networks with temporal input encodings C. W. Omlin and C. L. Giles and B. G. Horne and L. R. Leerink and T. Lin IEEE International Conference on Neural Networks (ICNN'94), pp. 1267-1278, IEEE Press, 1994. Palavras-chave: trainning, temporal encodings | |
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372 -Training Second-Order Recurrent Neural Networks Using Hints Christian W. Omlin and C. Lee Giles Machine Learning -- Proc. of the 9th Int. Workshop, pp. 361-366, Morgan Kaufman, July 1992. Palavras-chave: second-order alg., hints | |
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373 -The Dynamics of Discrete-Time Computation, With Application to Recurrent Neural Networks and Finite State Machine Extraction M. P. Casey Neural Computation, 8(6), pp. 1135-1178, 1996. Palavras-chave:discrete time, dynamic system | |
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374 -The Induction of Dynamical Recognizers J. B. Pollack Machine Learning, Vol. 7, Number 2/3, pp. 227-252, 1991. Palavras-chave: recognizers, dynamic system | |
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375 -The Problem of Learning Long-Term Dependencies in Recurrent Networks Y. Bengio and P. Frasconi and P. Simard IEEE International Conference on Neural Networks, Vol. III, pp. 1183-1188, IEEE Press, 1993. Palavras-chave: learning, | |
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376 -Three Constructive Algorithms for Network Learning Stephen I. Gallant Proceedings, 8th Annual Conference of the Cognitive Science Society, pp. 652-660, 1986. Palavras-chave: learning, construtive algorithm | |
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377 -The Cascade-Correlation Learning Architecture S. E. Fahlman Advances in Neural Information Processing Systems 2, pp. 524-532, Morgan Kaufmann Publishers, 1990. Palavras-chave: cascade correlation, learning, architeture | |
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378 -Training Recurrent Neural Networks: Why and How? An Illustration in Dynamical Process Modeling O. Nerrand and P. Roussel-Ragot and D. Urbani and L. Personnaz and G. Dreyfus IEEE Transactions on Neural Networks, 5(2), pp. 178-184, March 1994. Palavras-chave: training, dynamical process, | |
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379 -Use of Recurrent Neural Networks for Bioprocess Identification in On-Line Optimization by Micro-Genetic Algorithms M. N. Karim and S. L. Rivera Proceedings of the American Control Conference, Vol. 3, pp. 1931-1932, American Automatic Control Council, 1992. Palavras-chave: bioprocess, optimization, genetic , evolutionary | |
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380 -Using Hints to Successfully Learn Context-Free Grammars with a Neural Network Pushdown Automaton S. Das and C. L. Giles and G. Z. Sun Advances in Neural Information Processing Systems 5, pp. 65-72, Morgan Kaufmann Publishers, 1993. Palavras-chave: learning, grammar, automata | |
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381 -Using knowledge-based neural networks to improve algorithms: Refining the Chou-Fasman algorithm for protein folding R. Maclin and J. W. Shavlik Machine Learning, Vol. 11, pp. 195-215, 1993. Palavras-chave: Chou-Fasman algorithm, biomedic | |
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382 -Using Recurrent Neural Networks for Adaptive Communication Channel Equalization G. Kechriotis and E. Zervas and E. S. Manolakos IEEE Transactions on Neural Networks, 5(2), pp. 267-278, March 1994. Palavras-chave: communication, chanel equalization, adaptive system | |
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383 -Using Recurrent Neural Networks to Learn the Structure of Interconnection Networks M. W. Goudreau and C. L. Giles Neural Networks, 8(5), pp. 793-804, 1995. Palavras-chave: learning, training, connection | |
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384 -What Connectionist Models Learn: Learning and Representation in Connectionist Networks S. H. Hanson and D. J. Burr Neural Networks: Theory and Applications, pp. 169-208, Academic Press, 1991. Palavras-chave: models, learning, connectionist |
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