Revisão bibliográfica redes neurais recorrentes

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Palavras-chave: Hopfield net, convergence, discrete time systems

165-A novel adaptive filtering technique for the processing of abdominal fetal electrocardiogram using neural network

Selvan, S.; Srinivasan, R.

Adaptive Systems for Signal Processing, Communications, and Control Symposium 2000.

AS-SPCC. The IEEE 2000 , 2000

Page(s): 289 –292

Palavras-chave: adaptive filtering, fetal eletrogrardiogram,

166-A recurrent neural network for minimum infinity-norm kinematic control of redundant manipulators with an improved problem formulation and reduced architecture complexity

Wai Sum Tang; Jun Wang

Systems, Man and Cybernetics, Part B, IEEE Transactions on , Volume: 31 Issue: 1 ,

Feb 2001

Page(s): 98 –105

Palavras-chave:kinematic control, control,redundant manipulators

167-A recurrent neural network for online design of robust optimal filters

Danchi Jiang; Jun Wang

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on ,

Volume: 47 Issue: 6 , June 2000

Page(s): 921 –926

Palavras-chave:circuits, filters, optmal filters

168-A spatio temporal neural network on dynamic Gd-enhanced MR images for diagnosing recurrent nasal papilloma

Chuan-Yu Chang; Pau-Choo Chung; E-Liang Chen; Wen-Chen Huang; Ping-Hong Lai

Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual

International Conference of the IEEE , Volume: 4 , 2000

Page(s): 3056 -3059 vol.4

Palavras-chave: spatio temporal, dynamic Gd enhanced, MR images

169-A truncated normalized max product set of equations and its solution for a recurrent fuzzy neural network

Brouwer, R.K.

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th ,

Volume: 1 , 2001

Page(s): 529 –533

Palavras-chave:fuzzy, truncated normalized max product

170-Adaptive control for multi-machine power systems using genetic algorithm and neural network

Senjyu, T.; Yamane, S.; Uezato, K.

Power Engineering Society Winter Meeting, 2000. IEEE , Volume: 2 , 2000

Page(s): 1342 -1347 vol.2

Palavras-chave:power engineering, adaptive systems, genetic algorithms,

172-Adaptive hybrid control using recurrent-neural-network for linear synchronous motor servo drive system

Faa-Jeng Lin; Wen-Der Chou; Chih-Hong Lin

Electrical and Computer Engineering, 2001. Canadian Conference on , Volume: 1 , 2001

Page(s): 643 –648

Palavras-chave:control, adaptive systems, servo drive system

173-Adaptive recurrent-neural-network control for linear induction motor

Rong-Jong Wai; Chun-Ming Hong

Control Applications, 2000. Proceedings of the 2000 IEEE International Conference on ,

2000

Page(s): 184 –189

Palavras-chave:control, induction motor, linear induction

174-An artificial neural network model for generating periodic signals by synchronizing external stimuli

Fujimoto, K.; Cottenceau, G.; Akutagawa, M.; Nagashino, H.; Kinouchi, Y.

Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual

International Conference of the IEEE , Volume: 3 , 2000

Page(s): 1909 -1912 vol.3

Palavras-chave:periodic signals, symchronizing stimuli

175-An evolutionary active-vision system

Kato, T.; Floreano, D.

Evolutionary Computation, 2001. Proceedings of the 2001 Congress on , Volume: 1 ,

2001

Page(s): 107 -114 vol. 1

Palavras-chave:evolutionary, active system,

176-A trial activity enhancement by Wiener filtering using an artificial neural network

Vasquez, C.; Hernandez, A.; Mora, F.; Carrault, G.; Passariello, G.

Biomedical Engineering, IEEE Transactions on , Volume: 48 Issue: 8 , Aug. 2001

Page(s): 940 –944

Palavras-chave:wiener filtering, biomedical applications, Wiener

177-Combined identification of parameters and nonlinear characteristics based on input-output data

Hintz, C.; Rau, M.; Schroder, D.

Advanced Motion Control, 2000. Proceedings. 6th International Workshop on , 2000

Page(s): 175 -180

Palavras-chave:paramete identification, non linear systems, control

178-Contactless magnetic leadscrew: vibration control and resonance

compensation

Chang, T.; Dani, B.; Zhiming Ji; Caudill, R.

American Control Conference, 2000. Proceedings of the 2000 , Volume: 3 , 2000

Page(s): 2087 -2091 vol.3

Palavras-chave:control, vibration, ressonance

179-Dynamic wavelet neural network for nonlinear dynamic system identification

Yonghong Tan; Xuanju Dang; Feng Liang; Chun-Yi Su

Control Applications, 2000. Proceedings of the 2000 IEEE International Conference on ,

2000

Page(s): 214 –219

Palavras-chave:wavelet, non linear systems, system identification

180-Emergence of horizontal cells receptive fields spectral properties by de-correlation of cones spectral response functions

Iniushin, M.U.; Stankevich, A.A.

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on ,

Volume: 1 , 2001

Page(s): 99 -102 vol.1

Palavras-chave:spectral response, correlation

181-Enhancement of QRS complex using a neural network based ALE

Han-Go Choi; Eun-Bo Shim

Engineering in Medicine and Biology Society, 2000. Proceedings of the 22nd Annual

International Conference of the IEEE , Volume: 2 , 2000

Page(s): 958 -961 vol.2

Palavras-chave:biology, QRS, ALE

182-Generation of the sense of a sentence in Arabic language with a connectionist approach

Meftouh, K.; Laskri, M.T.

Computer Systems and Applications, ACS/IEEE International Conference on. 2001 ,

2001

Page(s): 125 –127

Palavras-chave:language, connection aproach recurrent neural net.

183-Generation scheduling with demand bids

Sheridan, W.P.; Flynn, M.E.; O'Malley, M.J.

Power Engineering Society Summer Meeting, 2000. IEEE , Volume: 4 , 2000

Page(s): 2109 -2114 vol. 4

Palavras-chave:power systems, schedulind, demand bids

184-Global stability analysis of discrete-time recurrent neural networks

Barabanov, N.E.; Prokhorov, D.V.

American Control Conference, 2001. Proceedings of the 2001 , Volume: 6 , 2001

Page(s): 4550 –4555

Palavras-chave: stability analysis, control

185-High speed directional element design and evaluation using neuro-computing technology

Sanaye-Pasand, M.; Malik, O.P.

Developments in Power System Protection, 2001, Seventh International Conference on

(IEE) , 2001

Page(s): 291 –294

Palavras-chave:power systems, control

186-Hybrid control for speed sensorless induction motor drive

Rong-Jong Wai

Fuzzy Systems, IEEE Transactions on , Volume: 9 Issue: 1 , Feb 2001

Page(s): 116 –138

Palavras-chave:control, induction motor, fuzzy

187-Hybrid control using recurrent fuzzy neural network for linear induction motor servo drive

Faa-Jeng Lin; Rong-Jong Wai

Fuzzy Systems, IEEE Transactions on , Volume: 9 Issue: 1 , Feb 2001

Page(s): 102 –115

Palavras-chave:control, fuzzy, linear induction motor

188-Identification of a nonlinear multi stand rolling system by a structured recurrent neural network

Hintz, C.; Rau, M.; Schroder, D.

Industry Applications Conference, 2000. Conference Record of the 2000 IEEE , Volume:

2 , 2000

Page(s): 1121 -1128 vol.2

Palavras-chave:non linear systems

189-Identification of dynamic systems using recurrent fuzzy neural network

Chih-Min Lin; Chun-Fei Hsu

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th , 2001

Page(s): 2671 –2675

Palavras-chave:dynamic systems, identification systems, fuzzy

190-Intelligent backstepping control for linear induction motor drive

Wai, R.-J.; Lin, F.-J.; Hsu, S.-P.

Control Theory and Applications, IEE Proceedings- , Volume: 148 Issue: 3 , May 2001

Page(s): 193 –202

Palavras-chave:control, motor, linear induction

191-Intelligent modeling, observation, and control for nonlinear systems

Schroder, D.; Hintz, C.; Rau, M.

Mechatronics, IEEE/ASME Transactions on , Volume: 6 Issue: 2 , June 2001

Page(s): 122 –131

Palavras-chave: control, non linear systems, artificial intelligence

192- A Learning Algorithm for Continually Running Fully Connected Recurrent Neural Networks

R. J. Williams and D. Zipser

Technical Report, Un. of California at San Diego, Number ICS-8805, 1988.

Palavras-chave: learning,

193 -Application of temporal neural networks to source localisation

B. Colnet and S. Durand

International Conference on Artificial Neural Networks and Genetic Algorithms, April 1995.

Palavras-chave: genetic, evolutionary, source localization

194- A Method for Training Recurrent Neural Networks for Classification by Building Basins of Attraction

R. K. Brouwer

Neural Networks, 8(4), pp. 597-603, 1995.

Palavras-chave: training, classification, atraction

195 - Alopex: A Correlation-Based Learning Algorithm for Feed-Forward and Recurrent Neural Networks

K. P. Unnikrishnan and K. P. Venugopal

Neural Computation, 6(3), pp. 469-490, 1994.

Palavras-chave: learning alg., feed-forward neural nets

196 -An algebraic framework to represent finite state automata in single-layer recurrent neural networks

R. Alquezar and A. Sanfeliu

Neural Computation, 7(5), pp. 931-949, 1995.

Palavras-chave: control, finite state automata, single layer

197- Absolute Stability Conditions for Discrete-Time Recurrent Neural Networks

Liang Jin and Peter N. Nikiforuk and Madan M. Gupta

IEEE Transactions on Neural Networks, 5(6), pp. 954-964, November 1994.

Palavras-chave: stability, discrete time, recurrent

198 -Adding Learning to Cellular Genetic Algorithms for Training Recurrent Neural Networks

K. W. C. Ku and M. W. Mak and W. C. Siu

IEEE-NN, 10(2), p. 239, March 1999.

Palavras-chave: learning, genetic, evolutionary

199 -A Fixed Size Storage Time Complexity Learning Algorithm for Fully Recurrent Continually Running Networks

J. H. Schmidhuber

Neural Computation, 4(2), pp. 243-248, 1992.

Palavras-chave: learning, storage

200 - A Framework of Combining Symbolic and Neural Learning

J. W. Shavlik

Technical Report, Computer Sciences Dept, U of Wisconson - Madison, Number TR 1123, Computer Sciences Dept., 1992.

Palavras-chave: learning, symbolic, recurrent

201 -A Learning Algorithm for Adaptive Time-Delays in a Temporal Neural Network

D. T. Lin and J. E. Dayhoff and P. A. Ligomenides

Technical Report, Systems Research Center, University of Maryland, Number TR 92-59, 1992.

Palavras-chave: learning, adaptive systems, time delay

202 -A learning algorithm for Boltzmann Machines

D. H. Ackley and G. E. Hinton and T. J. Sejnowski

Cognitive Science, Vol. 9, pp. 147-169, 1985.

Palavras-chave: learning, boltzman machines

203 -A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons

M. Marchand and M. Golea and P. Rujan

Europhysics Letters, Vol. 11, p. 487, 1990.

Palavras-chave: convergence, sequential, learning

204 -A Learning Method for Recurrent Networks Based on Minimization of Finite Automata

I. Noda and M. Nagao

Proceedings International Joint Conference on Neural Networks 1992, Vol. I, pp. 27-32, June 1992. Palavras-chave: learning, automata , finite automata

205 -A learning Rule for Asynchornous Perceptrons with Feedback in a Combinatorial Environment

L. B. Almeida

IEEE First Int. Conf. Neural Networks, pp. 609-618, 1987.

Palavras-chave: learning, asyncronous, feedback

206 -A Method for 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

Computational Learning Theory and Natural Learning Systems III, pp. 95-114, MIT Press, 1995.

Palavras-chave: learning, construtive learning, method

207 -A Neural Model for Category Learning

D. L. Reilly and L. N. Cooper and C. Elbaum

Biological Cybernetics, Vol. 45, pp. 35-41, 1982.

Palavras-chave: learning, category, model

208 -A Survey And Critique of Techniques For Extracting Rules From Trained Artificial Neural Networks

R. Andrews and J. Diederich and A. B. Tickle

Technical Report, Queensland University of Technology, Number QUTNRC-95-01-02, 1995.

Palavras-chave: survey, supervised, extracting rules

209 -A Unified Approach for Integrating Explicit Knowledge and Learning by Example in Recurrent Networks

P. Frasconi and M. Gori and M. Maggini and G. Soda

1991 IEEE INNS International Joint Conference on Neural Networks - Seattle, Vol. 1, pp. 811-816, IEEE Press, 1991.

Palavras-chave: learning, example, knowledge

210 -An Algebraic Framework to Represent Finite State Machines in Single-Layer Recurrent Neural Networks

R. Alquézar and A. Sanfeliu

Neural Computation, 7(5), pp. 931-949, 1995.

Palavras-chave: finite state machine, algebric, single -layer

211 -An Analysis of Noise in Recurrent Neural Networks: Convergence and Generalization

K.-C. Jim and C. L. Giles and B. G. Horne

IEEE Transactions on Neural Networks, 7(6), pp. 1424-1438, November 1996.

Palavras-chave: noise, convergence, generalization

212 -An Analysis of the Gamma Memory in Dynamic Neural Networks

J. C. Principe and J. M. Kuo and S. Celebi

IEEE Transactions on Neural Networks, 5(2), pp. 331-337, 1994.

Palavras-chave: gamma memory, memory, dynamical systems

213 -Associative Memory in a Simple Model of Oscillating Cortex

Bill Baird

Advances in Neural Information Processing Systems 2, pp. 68-75, Morgan Kaufmann Publishers, 1990.

Palavras-chave: memory, associative memory, model

214 -Application of temporal neural networks to source localisation

B. Colnet and S. Durand

International Conference on Artificial Neural Networks and Genetic Algorithms, April 1995.

Palavras-chave: temporal, source localization

215 -Biases in Inductive Learning: Introduction

Diana Gordon

Proceedings of the Machine Learning 1992 Workshop on Biases in Inductive Learning, 1992.

Palavras-chave: learning, bias, inductive learning

216 -Block-Structured Recurrent Neural Networks

S. Santini and Del A. Bimbo and R. Jain

Neural Networks, 8(1), pp. 135-147, 1995.

Palavras-chave: architeture, structured

217 -Can Recurrent Neural Networks Learn Natural Language Grammars?

Steve Lawrence and C. Lee Giles and Sandiway Fong

Proceedings of the IEEE International Conference on Neural Networks, pp. 1853-1858, IEEE Press, 1996.

Palavras-chave: grammar, language , learning

218 -Constructing deterministic finite-state automata in sparse recurrent neural networks

C. W. Omlin and C. L. Giles

IEEE International Conference on Neural Networks (ICNN'94), pp. 1732-1737, IEEE Press, 1994.

Palavras-chave: control, finite-state autonoma, sparse recurrente neural nets

219 - Constructive Learning of Recurrent Neural Networks

D. Chen and C. Lee Giles and G. Z. Sun and H. H. Chen and Y. C. Lee and M. W. Goudreau

Computational Learning Theory and Natural Learning Systems III, MIT Press, 1993.

Palavras-chave: learning, constructive

220 - 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, 1994.

Palavras-chave: Learning, control, cascade correlation

221 -Can Recurrent Neural Networks Learn Natural Language Grammars?

Steve Lawrence and C. Lee Giles and Sandiway Fong

Proceedings of the IEEE International Conference on Neural Networks, pp. 1853-1858, IEEE Press, 1996.

Palavras-chave: learning, grammar, language

222 -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

223 -Constructive Learning of Recurrent Neural Networks

D. Chen and C. Lee Giles and G. Z. Sun and H. H. Chen and Y. C. Lee and M. W. Goudreau

Computational Learning Theory and Natural Learning Systems III, MIT Press, 1993.

Palavras-chave: learning, learning systems

224 -Credit assignment through Time: Alternatives to Backpropagation

Yoshua Bengio and P. Frasconi

Advances in Neural Information Processing Systems 6, Morgan Kaufmann, 1994.

Palavras-chave: time, backpropagation, recurrent

225 -Combining Symbolic and Neural Learning

J. W. Shavlik

Machine Learning, 14(3), pp. 321-331, 1994.

Palavras-chave: symbol, learning, combining

226 -Comments on ``Constructive Learning of Recurrent Neural Networks: ...'', Cascading the Proof Describing Limitations of Recurrent Cascade Correlation

S. C. Kremer

IEEE Transactions on Neural Networks, 1996.

Palavras-chave: learning, construtive learning, limitations

227 -Comments On ``Diagonal Recurrent Neural Networks for Dynamic Systems Control''---Reproof of Theorems 2 and 4

X. Liang

IEEE Transactions on Neural Networks, 8(3), pp. 811-812, May 1997.

Palavras-chave: comments, diagonal neural net, dynamic systems, control

228 -Complexity of exact gradient computation algorithms for recurrent neural networks

R. J. Williams

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