Technical Report, College of Computer Science , Northeastern University, Number NUCCS8927, 1989.
Palavraschave: 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 UMIACSTR9512 and CSTR3408, 1995.
Palavraschave: NARX, capabilities

230  Connectionist Learning for Control
Andrew G. Barto
Neural Networks for Control, MIT Press, 1990.
Palavraschave: learning, connectionist, control

231 Connectionist Pushdown Automata that Learn ContextFree 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. 577580, Lawerence Erlbaum, 1990.
Palavraschave: connectionism, automata, grammar

232 Connectionist Recruitment Learning
J. Diederich
Proceedings of the 8th European Conference on Artificial Intelligence, 1988.
Palavraschave: connectinism, learning

233 Constructive Induction using KnowledgeBased Neural Networks
G. G. Towell and M. W. Craven and J. W. Shavlik
Eighth International Machine Learning Workshop, p. 213, Morgan Kaufmann Publishers, 1990.
Palavraschave: induction, constructive learning, learning

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. 11961201, IEEE Press, 1993.
Palavraschave: constructive learning, learning,

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. 829836, 1995.
Palavraschave: construtive learning, learning, casade correlation, correlation

236 Contour enhancement, short term memory, and constancies in reverberating neural networks
S. Grossberg
Studies in Applied Mathematics, 52(3), pp. 213257, The MIT Press, 1973.
Palavraschave: memory, short term memory,

237  Correction to Proof That Recurrent Neural Networks Can Robustly Recognize Only Regular Languages
Mike Casey
Neural Computation, 10(5), pp. 10671069, 1998.
Palavraschave: recognize pattern, pattern, languages

238 Daphne: Data Parallelism Neural Network Simulator
Paolo Frasconi and M. Gori and Giovanni Soda
Int. Journal of Modern Physics C, 4(1), pp. 1728, 1993.
Palavraschave: paralelism, Daphne, data

239 Dynamic Recurrent Neural Networks
Barak A. Pearlmutter
Technical Report, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, Number CMUCS90196, December 1990.
Palavraschave: dynamic systems

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.
Palavraschave: dynamic nets

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. 10331038, 1993.
Palavraschave: automata, pushdown, discrete nets

242 Diagonal Recurrent Neural Networks for Dynamic Systems Control
C.C. Ku and K. Y. Lee
IEEE Transactions on Neural Networks, 6(1), pp. 144156, January 1995.
Palavraschave: dynamic systems, diagonal neural net

243 Discontinuities in Recurrent Neural Networks
Ricard Gavalda and Hava T. Siegelmann
Neural Computation, 11(3), pp. 715746, 1999.
Palavraschave: discontinuities

244 Discovering the Structure of a SelfRouting Interconnection Network with a Recurrent Neural Network
M. W. Goudreau and C. L. Giles
International Workshop on Applications of Neural Networks to Telecommunications, pp. 5259, Lawrence Erlbaum, 1993.
Palavraschave: intercnnection, self routing, structure

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. 10331038, 1993.
Palavraschave: discretesystem, control, automata

246 Distributed Representations, Simple Recurrent Networks, and Grammatical Structure
J. L. Elman
Machine Learning, Vol. 7, Number 2/3, pp. 195226, 1991.
Palavraschave: grammatical structure, grammar, training

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.
Palavraschave: dynamic systems, applications, recurrent net.

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. 649656, MIT Press, 1995.
Palavraschave: noise, convergence, noise efects

249 Evaluation and selection of biases in machine learning
D. Gordon and M desJardins
Machine Learning, Vol. 20, Number 1,2, pp. 522, 1995.
Palavraschave: bias, learning, machine learning

250  Existence, Learning, and Replication of Periodic Motions in Recurrent Neural Networks
A. D. Ruiz and H. Owens and S. Townley
IEEENN, 9(4), p. 651, July 1998.
Palavraschave: learning, motion, existence

251 Experimental Analysis of the Realtime Recurrent Learning Algorithm
R. J. Williams and D. Zipser
Connection Science, 1(1), pp. 87111, 1989.
Palavraschave: realtime analysis, learning, analysis

252 Extended Kalman FilterBased Pruning Method for Recurrent Neural Networks
John Sum and Laiwan Chan and Chising Leung and Gilbert H. Young
Neural Computation, 10(6), pp. 14811505, 1998.
Palavraschave: Kalman filter, filter, pruning

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. 317324, Morgan Kaufmann Publishers, 1992.
Palavraschave: learning, grammar, extracting

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. 849872, 1993.
Palavraschave: recognition, architeture, order

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.
Palavraschave: extration, insertion, information

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 2122 1997.
Palavraschave: rule, time series, prediction

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. 849872, 1993.
Palavraschave: order, comparison, architeture

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
Palavraschave: sequntioal , radial base function

259 Extraction, Insertion and Refinement of Symbolic Rules in DynamicallyDriven Recurrent Neural Networks
C. L. Giles and C. W. Omlin
Connection Science, Vol. 5, Number 3,4, pp. 307337, 1993.
Palavraschave: symbolic rules, extration

260 FaultTolerant Implementation of FiniteState Automata in Recurrent Neural Networks
C. W. Omlin and C. L. Giles
Technical Report, Rensselaer Polytechnic Institute, Number TR CS 953, 1995.
Palavraschave: fault, automata, finite state

261 Finite State Automata and Connectionist Machines: A Survey
M. A. Castaño
Technical Report, Universitat Jaume I, Departament d'Informàtica, Number DI 0305/95, 1995.
Palavraschave: finite state, connectionist, automata

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 UMIACSTR951 and CSTR3396, 1995.
Finitte state, automata, dynamical systems

263  FirstOrder Recurrent Neural Networks and Deterministic Finite State Automata
Peter Manolios and Robert Fanelli
Neural Computation, 6(6), pp. 11551173, 1994.
Palavraschave:automata, finite state, first order neural net.

264 Fixed Point Analysis for Recurrent Neural Networks
M. B. Ottaway
Advances in Neural Information Processing Systems I, Morgan Kauffman, 1989.
Palavraschave: analysis, fixed point

265  FaultTolerant Implementation of FiniteState Automata in Recurrent Neural Networks
C. W. Omlin and C. L. Giles
Technical Report, Computer Science Department, Rensselaer Polytechnic Institute, Number 953, 1995.
Palavraschave: finite automata, fault tolerant

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 UMIACSTR951 and CSTR3396, 1995.
Palavraschave: fine state, automata, dynamical system

267  Fuzzy Finitestate 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.
Palavraschave: fuzzy, finite state, code

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.
Palavraschave: codification

269 Graded State Machine: The Representation of Temporal Contingencies in Simple Recurrent Networks
D. ServanSchreiber and A. Cleeremans and J. L. McClelland
Machine Learning, Vol. 7, p. 161, 1991.
Palavraschave: machine learning, graded state machine, temporal analysis

270 Gradient calculation for dynamic recurrent neural networks: a survey
Barak A. Pearlmutter
IEEE Transactions on Neural Networks, 6(5), pp. 12121228, 1995.
Palavraschave: dynamic systems, gradient method

271 GradientBased Learning Algorithms for Recurrent Connectionist Networks
R. J. Williams and D. Zipser
Technical Report, Computer Science, Northeastern University, Number NUCCS909, 1990.
Palavraschave: learning, gradient method, connectionist net

272 Gradientbased learning algorithms for recurrent networks and their computational complexity
R. J. Williams and D. Zipser
Backpropagation: Theory, Architectures and Applications, pp. 433486, Lawrence Erlbaum Publishers, 1995.
Palavraschave: learning, gradient mehod, complexity

273 Grammatical Inference
L. Miclet
Syntactic and Structural Pattern Recognition; Theory and Applications, World Scientific, 1990.
Palavraschave: grammar, learning

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. 126129, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavraschave: growing, statistic, units

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 UMIACSTR9676 and CSTR3706, 1996.
Palavraschave: memory, NARX nets, memory orders

276 Heuristics for the Extraction of Rules from DiscreteTime 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. 3338, June 1992.
Palavraschave: rules, discretetime, heuristics

277 How DiscreteTime Recurrent Neural Networks Work
Mike Casey
Technical Report, University of California, Department of Mathematics San Diego",, Number INC9503, April 1995.
Palavraschave: discretetime

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. 489499, 1998.
Palvraschave: electronics, learning self organizing map

279 Implementation of Parallel Thinning Algorithms Using Recurrent Neural Networks
Raghu Krishnapuram and LingFan Chen
IEEE Transactions on Neural Networks, 4(1), pp. 142147, January 1993.
Palavraschave: parralel thinking, implementation

280 Implementing Semantic Networks in Parallel Hardware
G. E. Hinton
Parallel Models of Associative Memory, pp. 161187, Lawrence Erlbaum Publishers, 1981.
Palavraschave: semantic net, hardware, parallel

281  Improved Phoneme Recognition using MultiModule Recurrent Neural Networks
L. R. Leerink and M. Jabri
Proceedings of the Fourth Australian Conference on Neural Networks, pp. 2629, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavraschave: recognition, multi module, pattern recognition

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.
Palavraschave: learning, incremental learning

283  Inductive Learning in Symbolic Domains Using StructureDriven Recurrent Neural Networks
Andreas Küchler and Christoph Goller
KI96: Advances in Artificial Intelligence, Lecture Notes in Computer Science (LNCS 1137), pp. 183197, Springer, 1996.
Palavraschave: learning, indutive learning, structure driven net

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 DSIRT15/92, August 1992.
Palavraschave: automata, finite state, non deterministic systems

285  Intelligent control using neural networks
K. S. Narendra and SM. Li and J. B. D. Cabrera
Proceedings of the Eighth Yale Workshop on Adaptive and Learning Systems, pp. 142149, 1994.
Palavraschave: control,

286 Implementation of Parallel Thinning Algorithms Using Recurrent Neural Networks
Raghu Krishnapuram and LingFan Chen
IEEE Transactions on Neural Networks, 4(1), pp. 142147, January 1993.
Palavraschave: training, learning

287 Improved Phoneme Recognition using MultiModule Recurrent Neural Networks
L. R. Leerink and M. Jabri
Proceedings of the Fourth Australian Conference on Neural Networks, pp. 2629, Dept. of Electrical Engineering, U. of Sydney, 1993.
Palavraschave: recognition, multi module, grammar

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. 1322, IEEE Press, 1992.
Palavraschave: symbolic rules

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 UMIACSTR9494 and CSTR3328, 1994.
Palavraschave: learning, finite state machine, machine learning

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. 13591365, 1995.
Palavraschave: learning, finite state machine, machine learning

291 –Learning and Extracting Finite State Automata with SecondOrder 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. 393405, 1992.
Palavraschave: learning, automata, finite state

292 Learning and Extracting Initial Mealy Machines With a Modular Neural Network Model
P. Tino and J. Sajda
Neural Computation, 7(4), pp. 822844, 1995.
Palavraschave: learning, mealy machine, modular neural net

293  Learning Automata from Ordered Examples
S. Porat and J. A. Feldman
Machine Learning, Vol. 7, Number 23, pp. 109138, 1991.
Palavraschave: learning, automata, machine learning

294 Learning Contextfree 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. 791795, Morgan Kaufmann Publishers, 1992.
Palavraschave: learning, grammars, stack memory

295  Learning Finite State Machines with SelfClustering Recurrent Networks
Z. Zeng and R. M. Goodman and P. Smyth
Neural Computation, 5(6), pp. 976990, 1993.
Palavraschave: learning, finite state machine, clustering

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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