A multiobjective genetic algorithm for obtaining the optimal size of a recurrent neural network for grammatical inference

Pattern Recognition

Volumn 38, Issue 9, 2005, Pages 1444-1456

  Delgado, M ^a   Pegalajar, M C ^a  

^a UNIVERSITY OF GRANADA   (Spain)

Author keywords

Grammatical inference; Multiobjective genetic algorithm; Recurrent neural networks

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; GENETIC ALGORITHMS; LEARNING ALGORITHMS; RECURRENT NEURAL NETWORKS; SELF ORGANIZING MAPS; SET THEORY; TOPOLOGY;

COMPUTATIONAL TIME; GRAMMATICAL INFERENCE; MULTIOBJECTIVE GENETIC ALGORITHM; OPTIMAL TOPOLOGIES;

COMPUTATIONAL GRAMMARS;

EID: 19944375499     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patcog.2004.03.026     Document Type: Article

References (41)

1. D. Angluin, and C.H. Smitch Inductive inference theory and methods ACM Comput. Surv. 15 3 1983 237
2. H.S. Fu Syntactic Pattern Recognition and Applications 1982 Prentice-Hall Englewood Cliffs, New Jersey
3. M.H. Harrison Introduction to Formal Language Theory 1978 Addison-Wesley Reading, MA
4. K.J. Lang Random DFA's can be approximately learned from sparse uniform sample Proceedings of the Fifth ACM workshop on Computational Learning Theory 1992 45 52
5. A.L. Oliveira, (Ed.), Gramatical Inference: Algorithms and Applications, Lecture Notes in Computer Science, vol. 1891, Springer, Berlin, September 2000.
6. P.J. Angeline, G.M. Saunders, and J.B. Pollack An evolutionary algorithm that constructs recurrent neural networks IEEE Trans. Neural Networks 5 1 1994 54 65
7. A. Blanco, M. Delgado, and M.C. Pegalajar Fuzzy grammatical inference using neural network Mathware Soft Comput. 5 2-3 1998 133 140
8. A. Blanco, M. Delgado, and M.C. Pegalajar Extracting rules from a (fuzzy/crisp) recurrent neural networks using a self-organizing map Int. J. Intell. Syst. 15 7 2000 595 621
9. A. Blanco, M. Delgado, and M.C. Pegalajar A genetic algorithm to obtain the optimal recurrent neural network Int. J. Approx. Reasoning 23 2000 67 83
10. A. Blanco, M. Delgado, and M.C. Pegalajar A real-coded genetic algorithm for training recurrent neural networks Neural Networks 14 2001 93 105
11. A. Blanco, M. Delgado, and M.C. Pegalajar Fuzzy automaton induction using neural networks Int. J. Approx. Reasoning 27 1 2001 1 26
12. A. Blanco, M. Delgado, M.C. Pegalajar, I. Requena, Speech recognition using fuzzy second-order recurrent neural networks, Connectionist models of neurons, learning processes and artificial intelligence, Sixth International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001, Granada, Spain, June 13-15, Proceedings, Part I, 2001, pp. 285.
13. A. Cleeremans, D. Servan-Schreiber, and J. McClelland Finite state automata and simple recurrent networks Neural Comput. 1 3 1989 372 381
14. C.L. Giles, D. Chen, C.B. Miller, H.H. Chen, G.Z. Sun, and Y.C. Lee Second-order recurrent neural networks for grammatical inference in: Proceedings of the International Joint Conference Neural Networks vol. II 1991 273 281
15. C.L. Giles, D. Chen, G.Z. Sun, H. Chen, Y.C. Lee, and M.W. Goudreau Constructive learning of recurrent neural networks problems with recurrent cascade correlation and a simple solution IEEE Trans. Neural Networks 6 4 1995 829
16. C.L. Giles, C.B. Miller, D. Chen, H.H. Chen, G.Z. Sun, and Y.C. Lee Learning and extracting finite state automata with second order recurrent neural networks Neural Comput. 4 1992 393 405
17. C.L. Giles, and C. Omlin Pruning recurrent neural networks for improved generalization performance IEEE Trans. Neural Network 5 5 1994 848
18. C.W. Omlin, and C.L. Giles Extraction of rules from discrete-time recurrent neural network Neural Networks 9 1 1996 41 52
19. Z. Zeng, R. Goodman, and P. Smyth Learning finite state machines with self-clustering recurrent networks Neural Comput. 5 6 1993 976 990
20. A. Zeng, R. Goodman, and P. Smyth Discrete recurrent neural network, Neural Networks for grammatical inference IEEE Trans. Neural Networks 5 2 1994 320 330
21. H. Bourlard, and C. Wellekens Speech pattern discrimination and multi-layered perceptrons Comput. Speech Language 3 1998 1 19
22. Y. Le Cun, B. Boser, J. Denker, D. Henderson, Howard, W. Hubbard, L. Jackel, Backpropagation applied to handwritten zip code recognition, Neural Comput. 1 (1989) 541-551.
23. T. Sejnowski, and C. Rosenberg Parallel networks that learn to pronounce English text Complex Syst. 1 1987 145 168
24. S. Solla, Capacity control in classifiers for pattern recognition, in: Neural Networks for Signal Processing II, Proceedings of the 1992 IEEE Workshop, pp. 255-266.
25. A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K. Lang Phoneme recognition using time delay neural networks IEEE Trans. Acoust. Speech Signal Process. 37 1989 328 339
26. S. Hochreiter, and J. Schmidhuber Long short-term memory Neural Comput. 9 1997 1681 1726
27. R. Alquézar, A. Sanfeliu, A hybrid connectonist-symbolic approach to regular grammatical inference based on neural learning and hierarchical clustering, in: R.C. Carrasco, J. Oncina (Eds.), Grammatical Inference and Applications LNA, vol. 862, Springer, Berlin, pp. 203-211.
28. J.N. Morse Reducing the size of the nondominated set, Pruning by clustering Comput. Oper. Res. 7 1-2 1980 55 66
29. S. Fahlman The recurrent cascade-correlation architecture Advances in Neural Information Processing Systems vol.3 1991 Morgan Kaufmann Los Altos, CA 180 196
30. H. Kirschning, H. Tomabechi, J.I. Aoe, A parallel recurrent cascade-correlation neural network with natural connectionst glue, in: Proceeding of the IEEE Information Conference On Neural Networks ICNN'95, vol. 2, Perth, Australia, 1995, pp. 953-956.
31. S. Ribeiro, R. Alquézar, Incremental construction of LSTM recurrent neural network, in: Proceedings of the VII Congreso Iberoamericano de Reconocimiento de Patrones, CIARP'02, Ciudad de México, México. pp. 171-184.
32. J. Schmidhuber A fixed size storage O (n 3) time complexity learning algorithm for fully recurrent continually running networks Neural Comput. 4 1992 243 248
33. R.J. Williams, and D. Zipser A learning algorithm for continually running fully recurrent neural networks Neural Comput. 1 1989 270 280
34. R.J. Williams, and J. Peng An efficient gradient-based algorithm for on-line training of recurrent network trajectories Neural Comput. 2 1990 491 501
35. P. Mozer, Smolensky, Skeletonization: a technique for trimming the fat from a network via relevance assessment, Connection Sci. 11 (1989) 3-26.
36. E. Zizler, and T. Lothar Multi-objective evolutionary algorithms a comparative case study and the strength Pareto approach IEEE Trans. Evol. Comput. 3 4 1999 257 271
37. K. Deb Multi-objective genetic algorithms problem difficulties and construction of test problems Evol. Comput. 7 3 1999 205 230
38. A. Wright Genetic algorithms for real parameter optimisation, Foundations of genetic algorithms G.J.E. Rawlin First Workshop on the Foundations of Genetics Algorithms and Classifier Systems 1990 Morgan Kaufmann Los Altos, CA 205 218
39. M.A. Rosenman, and J.S. Gero Reducing the Pareto optimal set in multicriteria optimization Eng. Optim. 8 1985 189 206
40. T. Kohonen The self-organizing map Proc. IEEE 9 1990 1464 1480
41. J.E. Hopcroft, and J.F. Ullmann Introduction to Automata Theory, Languages and Computation 1979 Addison-Wesley Reading