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Technical Report, College of Computer Science , Northeastern University, Number NU-CCS-89-27, 1989.
Palavras-chave: gradient, algorithms, complexity
229 -Computational capabilities of recurrent NARX neural networks
H. T. Siegelmann and B. G. Horne and C. L. Giles
Technical Report, University of Maryland Department of Computer Science, Number UMIACS-TR-95-12 and CS-TR-3408, 1995.
Palavras-chave: NARX, capabilities
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230 - Connectionist Learning for Control
Andrew G. Barto
Neural Networks for Control, MIT Press, 1990.
Palavras-chave: learning, connectionist, control
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231 -Connectionist Pushdown Automata that Learn Context-Free Grammars
G. Z. Sun and H. H. Chen and C. L. Giles and Y. C. Lee and D. Chen
International Joint Conference on Neural Networks Jan. 1990",, Vol. I, pp. 577-580, Lawerence Erlbaum, 1990.
Palavras-chave: connectionism, automata, grammar
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232 -Connectionist Recruitment Learning
J. Diederich
Proceedings of the 8th European Conference on Artificial Intelligence, 1988.
Palavras-chave: connectinism, learning
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233 -Constructive Induction using Knowledge-Based Neural Networks
G. G. Towell and M. W. Craven and J. W. Shavlik
Eighth International Machine Learning Workshop, p. 213, Morgan Kaufmann Publishers, 1990.
Palavras-chave: induction, constructive learning, learning
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234 -Constructive Learning of Recurrent Neural Networks
D. Chen and C. L. Giles and G. Z. Sun and H. H. Chen and Y. C. Lee and M. W. Goudreau
1993 IEEE International Conference on Neural Networks, Vol. III, pp. 1196-1201, IEEE Press, 1993.
Palavras-chave: constructive learning, learning,
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235 -Constructive Learning of Recurrent Neural Networks: Limitations of Recurrent Casade Correlation and a Simple Solution
C. L. Giles and D. Chen and G. Z. Sun and H. H. Chen and Y. C. Lee and M. W. Goudreau
IEEE Transactions on Neural Networks, 6(4), pp. 829-836, 1995.
Palavras-chave: construtive learning, learning, casade correlation, correlation
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236 -Contour enhancement, short term memory, and constancies in reverberating neural networks
S. Grossberg
Studies in Applied Mathematics, 52(3), pp. 213-257, The MIT Press, 1973.
Palavras-chave: memory, short term memory,
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237 - Correction to Proof That Recurrent Neural Networks Can Robustly Recognize Only Regular Languages
Mike Casey
Neural Computation, 10(5), pp. 1067-1069, 1998.
Palavras-chave: recognize pattern, pattern, languages
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238 -Daphne: Data Parallelism Neural Network Simulator
Paolo Frasconi and M. Gori and Giovanni Soda
Int. Journal of Modern Physics C, 4(1), pp. 17-28, 1993.
Palavras-chave: paralelism, Daphne, data
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239 -Dynamic Recurrent Neural Networks
Barak A. Pearlmutter
Technical Report, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Number CMU-CS-90-196, December 1990.
Palavras-chave: dynamic systems
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240 -Dynamic Recurrent Neural Networks: Theory and Applications
C. L. Giles and G. M. Kuhn and R. J. Williams
IEEE Transactions on Neural Networks, 5(2), 1994.
Palavras-chave: dynamic nets
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241 -Discrete Recurrent Neural Networks as Pushdown Automata
Z. Zeng and R. M. Goodman and P. Smyth
International Symposium on Nonlinear Theory and its Applications, Vol. 3, pp. 1033-1038, 1993.
Palavras-chave: automata, pushdown, discrete nets
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242 -Diagonal Recurrent Neural Networks for Dynamic Systems Control
C.-C. Ku and K. Y. Lee
IEEE Transactions on Neural Networks, 6(1), pp. 144-156, January 1995.
Palavras-chave: dynamic systems, diagonal neural net
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243 -Discontinuities in Recurrent Neural Networks
Ricard Gavalda and Hava T. Siegelmann
Neural Computation, 11(3), pp. 715-746, 1999.
Palavras-chave: discontinuities
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244 -Discovering the Structure of a Self-Routing Interconnection Network with a Recurrent Neural Network
M. W. Goudreau and C. L. Giles
International Workshop on Applications of Neural Networks to Telecommunications, pp. 52-59, Lawrence Erlbaum, 1993.
Palavras-chave: intercnnection, self routing, structure
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245 -Discrete Recurrent Neural Networks as Pushdown Automata
Z. Zeng and R. M. Goodman and P. Smyth
International Symposium on Nonlinear Theory and its Applications, Vol. 3, pp. 1033-1038, 1993.
Palavras-chave: discretesystem, control, automata
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246 -Distributed Representations, Simple Recurrent Networks, and Grammatical Structure
J. L. Elman
Machine Learning, Vol. 7, Number 2/3, pp. 195-226, 1991.
Palavras-chave: grammatical structure, grammar, training
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247 - Dynamic Recurrent Neural Networks: Theory and Applications
C. L. Giles and G. M. Kuhn and R. J. Williams
IEEE Transactions on Neural Networks, 5(2), 1994.
Palavras-chave: dynamic systems, applications, recurrent net.
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248 -Effects of noise on convergence and generalization in recurrent networks
K. Jim and B. G. Horne and C. L. Giles
Advances in Neural Information Processing Systems 7, pp. 649-656, MIT Press, 1995.
Palavras-chave: noise, convergence, noise efects
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249 -Evaluation and selection of biases in machine learning
D. Gordon and M desJardins
Machine Learning, Vol. 20, Number 1,2, pp. 5-22, 1995.
Palavras-chave: bias, learning, machine learning
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250 - Existence, Learning, and Replication of Periodic Motions in Recurrent Neural Networks
A. D. Ruiz and H. Owens and S. Townley
IEEE-NN, 9(4), p. 651, July 1998.
Palavras-chave: learning, motion, existence
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251 -Experimental Analysis of the Real-time Recurrent Learning Algorithm
R. J. Williams and D. Zipser
Connection Science, 1(1), pp. 87-111, 1989.
Palavras-chave: real-time analysis, learning, analysis
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252 -Extended Kalman Filter-Based Pruning Method for Recurrent Neural Networks
John Sum and Lai-wan Chan and Chi-sing Leung and Gilbert H. Young
Neural Computation, 10(6), pp. 1481-1505, 1998.
Palavras-chave: Kalman filter, filter, pruning
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253 - Extracting and Learning an Unknown Grammar with Recurrent Neural Networks
C. L. Giles and C. B. Miller and D. Chen and G. Z. Sun and H. H. Chen and Y. C. Lee
Advances in Neural Information Processing Systems 4, pp. 317-324, Morgan Kaufmann Publishers, 1992.
Palavras-chave: learning, grammar, extracting
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254 -Experimental Comparison of the Effect of Order in Recurrent Neural Networks
C. B. Miller and C. Lee Giles
International Journal of Pattern Recognition and Artifical Intelligence, 7(4), pp. 849-872, 1993.
Palavras-chave: recognition, architeture, order
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255 -Extraction and Insertion of Symbolic Information in Recurrent Neural Networks
C. W. Omlin and C. Lee Giles
Artificial Intelligence and Neural Networks: Steps Toward Principled Integration, Volume 1: Basic Paradigms; Learning Representational Issues; and Integrated Architectures, Academic Press, 1994.
Palavras-chave: extration, insertion, information
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256 -Extraction of Rules from Recurrent Neural Networks and Applications to Financial Time Series
C. Lee Giles and Steve Lawrence and Ah Chung Tsoi
Meeting of the American Mathematical Society, March 21-22 1997.
Palavras-chave: rule, time series, prediction
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257 -Experimental Comparison of the Effect of Order in Recurrent Neural Networks
C. B. Miller and C. Lee Giles
International Journal of Pattern Recognition and Artifical Intelligence, 7(4), pp. 849-872, 1993.
Palavras-chave: order, comparison, architeture
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258 -Extraction of High Level Sequential Structure Using Recurrent Neural Networks and Radial Basis Functions
L. R. Leerink and M. Jabri
New Zealand International Conference on Artificial Neural Networks and Expert Systems, 1993
Palavras-chave: sequntioal , radial base function
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259 -Extraction, Insertion and Refinement of Symbolic Rules in Dynamically-Driven Recurrent Neural Networks
C. L. Giles and C. W. Omlin
Connection Science, Vol. 5, Number 3,4, pp. 307-337, 1993.
Palavras-chave: symbolic rules, extration
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260 -Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks
C. W. Omlin and C. L. Giles
Technical Report, Rensselaer Polytechnic Institute, Number TR CS 95-3, 1995.
Palavras-chave: fault, automata, finite state
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261 -Finite State Automata and Connectionist Machines: A Survey
M. A. Castaño
Technical Report, Universitat Jaume I, Departament d'Informàtica, Number DI 03-05/95, 1995.
Palavras-chave: finite state, connectionist, automata
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262 -Finite State Machines and Recurrent Neural Networks -- Automata and Dynamical Systems Approaches
Peter Tino and Bill G. Horne and C. Lee Giles
Technical Report, University of Maryland, Number TECHNICAL REPORT UMIACS-TR-95-1 and CS-TR-3396, 1995.
Finitte state, automata, dynamical systems
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263 - First-Order Recurrent Neural Networks and Deterministic Finite State Automata
Peter Manolios and Robert Fanelli
Neural Computation, 6(6), pp. 1155-1173, 1994.
Palavras-chave:automata, finite state, first order neural net.
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264 -Fixed Point Analysis for Recurrent Neural Networks
M. B. Ottaway
Advances in Neural Information Processing Systems I, Morgan Kauffman, 1989.
Palavras-chave: analysis, fixed point
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265 - Fault-Tolerant Implementation of Finite-State Automata in Recurrent Neural Networks
C. W. Omlin and C. L. Giles
Technical Report, Computer Science Department, Rensselaer Polytechnic Institute, Number 95-3, 1995.
Palavras-chave: finite automata, fault tolerant
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266 -Finite State Machines and Recurrent Neural Networks -- Automata and Dynamical Systems Approaches
Peter Tino and Bill G. Horne and C. Lee Giles
Technical Report, University of Maryland, Number TECHNICAL REPORT UMIACS-TR-95-1 and CS-TR-3396, 1995.
Palavras-chave: fine state, automata, dynamical system
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267 - Fuzzy Finite-state Automata Can Be Deterministically Encoded into Recurrent Neural Networks
C. W. Omlin and K. K. Thornber and C. L. Giles
IEEE Transactions on Fuzzy Systems, 1997.
Palavras-chave: fuzzy, finite state, code
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268 -Forcing Simple Recurrent Neural Networks to Encode Context
A. Maskara and A. Noetzel
Proceedings of the 1992 Long Island Conference on Artificial Intelligence and Computer Graphics, 1992.
Palavras-chave: codification
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269 -Graded State Machine: The Representation of Temporal Contingencies in Simple Recurrent Networks
D. Servan-Schreiber and A. Cleeremans and J. L. McClelland
Machine Learning, Vol. 7, p. 161, 1991.
Palavras-chave: machine learning, graded state machine, temporal analysis
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270 -Gradient calculation for dynamic recurrent neural networks: a survey
Barak A. Pearlmutter
IEEE Transactions on Neural Networks, 6(5), pp. 1212-1228, 1995.
Palavras-chave: dynamic systems, gradient method
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271 -Gradient-Based Learning Algorithms for Recurrent Connectionist Networks
R. J. Williams and D. Zipser
Technical Report, Computer Science, Northeastern University, Number NU-CCS-90-9, 1990.
Palavras-chave: learning, gradient method, connectionist net
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272 -Gradient-based learning algorithms for recurrent networks and their computational complexity
R. J. Williams and D. Zipser
Back-propagation: Theory, Architectures and Applications, pp. 433-486, Lawrence Erlbaum Publishers, 1995.
Palavras-chave: learning, gradient mehod, complexity
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273 -Grammatical Inference
L. Miclet
Syntactic and Structural Pattern Recognition; Theory and Applications, World Scientific, 1990.
Palavras-chave: grammar, learning
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274 -Growing Context Units in Simple Recurrent Networks Using the Statistical Attribute of Weight Updates
L. R. Leering and M. A. Jabri
Proceedings of the Fourth Australian Conference on Neural Networks, pp. 126-129, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavras-chave: growing, statistic, units
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275 -How Memory Orders Effect the Performance of NARX Networks
Tsungnan Lin and B. G. Horne and C. L. Giles and S. Y. Kung
Technical Report, Institute for Advanced Computer Studies, University of Maryland, Number UMIACS-TR-96-76 and CS-TR-3706, 1996.
Palavras-chave: memory, NARX nets, memory orders
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276 -Heuristics for the Extraction of Rules from Discrete-Time Recurrent Neural Networks
C. W. Omlin and C. Lee Giles and C. B. Miller
Proceedings International Joint Conference on Neural Networks 1992, Vol. I, pp. 33-38, June 1992.
Palavras-chave: rules, discrete-time, heuristics
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277 -How Discrete-Time Recurrent Neural Networks Work
Mike Casey
Technical Report, University of California, Department of Mathematics San Diego",, Number INC-9503, April 1995.
Palavras-chave: discrete-time
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278 -Heterogeneous recurrent neural networks
Jenn Huei Jerry Lin and Jyh Shan Chang and Tzi Dar Chiueh
IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences" Number 3, pp. 489-499, 1998.
Palvras-chave: electronics, learning self organizing map
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279 -Implementation of Parallel Thinning Algorithms Using Recurrent Neural Networks
Raghu Krishnapuram and Ling-Fan Chen
IEEE Transactions on Neural Networks, 4(1), pp. 142-147, January 1993.
Palavras-chave: parralel thinking, implementation
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280 -Implementing Semantic Networks in Parallel Hardware
G. E. Hinton
Parallel Models of Associative Memory, pp. 161-187, Lawrence Erlbaum Publishers, 1981.
Palavras-chave: semantic net, hardware, parallel
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281 - Improved Phoneme Recognition using Multi-Module Recurrent Neural Networks
L. R. Leerink and M. Jabri
Proceedings of the Fourth Australian Conference on Neural Networks, pp. 26-29, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavras-chave: recognition, multi module, pattern recognition
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282 - Incremental learning, or the importance of starting small
J. L. Elman
Technical Report, Center for Research in Language, University of California at San Diego, Number CRL Tech Report 9101, 1991.
Palavras-chave: learning, incremental learning
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283 - Inductive Learning in Symbolic Domains Using Structure-Driven Recurrent Neural Networks
Andreas Küchler and Christoph Goller
KI-96: Advances in Artificial Intelligence, Lecture Notes in Computer Science (LNCS 1137), pp. 183-197, Springer, 1996.
Palavras-chave: learning, indutive learning, structure driven net
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284 -Injecting Nondeterministic Finite State Automata into Recurrent Neural Networks
P. Frasconi and M. Gori and G. Soda
Technical Report, Dipartimento di Sistemi e Informatica, Number DSI-RT15/92, August 1992.
Palavras-chave: automata, finite state, non deterministic systems
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285 - Intelligent control using neural networks
K. S. Narendra and S-M. Li and J. B. D. Cabrera
Proceedings of the Eighth Yale Workshop on Adaptive and Learning Systems, pp. 142-149, 1994.
Palavras-chave: control,
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286 -Implementation of Parallel Thinning Algorithms Using Recurrent Neural Networks
Raghu Krishnapuram and Ling-Fan Chen
IEEE Transactions on Neural Networks, 4(1), pp. 142-147, January 1993.
Palavras-chave: training, learning
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287 -Improved Phoneme Recognition using Multi-Module Recurrent Neural Networks
L. R. Leerink and M. Jabri
Proceedings of the Fourth Australian Conference on Neural Networks, pp. 26-29, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavras-chave: recognition, multi module, grammar
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288 -Inserting Rules into Recurrent Neural Networks
C. L. Giles and C. W. Omlin
Neural Networks for Signal Processing II, Proceedings of The 1992 IEEE Workshop, pp. 13-22, IEEE Press, 1992.
Palavras-chave: symbolic rules
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289 -Learning a Class of Large Finite State Machines with a Recurrent Neural Network
C. L. Giles and B. G. Horne and T. Lin
Technical Report, Institute for Advanced Computer Studies, University of Maryland, Number UMIACS-TR-94-94 and CS-TR-3328, 1994.
Palavras-chave: learning, finite state machine, machine learning
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290 -Learning a Class of Large Finite State Machines with a Recurrent Neural Network
C. L. Giles and B. G. Horne and T. Lin
Neural Networks, 8(9), pp. 1359-1365, 1995.
Palavras-chave: learning, finite state machine, machine learning
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291 –Learning and Extracting Finite State Automata with Second-Order Recurrent Neural Networks
C. L. Giles and C. B. Miller and D. Chen and H. H. Chen and G. Z. Sun and Y. C. Lee
Neural Computation, 4(3), pp. 393-405, 1992.
Palavras-chave: learning, automata, finite state
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292 -Learning and Extracting Initial Mealy Machines With a Modular Neural Network Model
P. Tino and J. Sajda
Neural Computation, 7(4), pp. 822-844, 1995.
Palavras-chave: learning, mealy machine, modular neural net
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293 - Learning Automata from Ordered Examples
S. Porat and J. A. Feldman
Machine Learning, Vol. 7, Number 2-3, pp. 109-138, 1991.
Palavras-chave: learning, automata, machine learning
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294 -Learning Context-free Grammars: Limitations of a Recurrent Neural Network with an External Stack Memory
S. Das and C. L. Giles and G. Z. Sun
Proceedings of The Fourteenth Annual Conference of the Cognitive Science Society, pp. 791-795, Morgan Kaufmann Publishers, 1992.
Palavras-chave: learning, grammars, stack memory
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295 - Learning Finite State Machines with Self-Clustering Recurrent Networks
Z. Zeng and R. M. Goodman and P. Smyth
Neural Computation, 5(6), pp. 976-990, 1993.
Palavras-chave: learning, finite state machine, clustering
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296 -Learning Finite State Transducers with a Recurrent Neural Network
D. Chen and C. L. Giles and G. Z. Sun and H. H. Chen and Y. C. Lee |
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