Training and extraction of fuzzy finite state automata in recurrent neural networks

Proceedings of the 2nd IASTED International Conference on Computational Intelligence, CI 2006

Volumn , Issue , 2006, Pages 271-275

  Chandra, Rohitash ^a   Omlin, Christian W ^b  

^a University of Fiji   (Fiji)

^b UNIVERSITY OF THE SOUTH PACIFIC   (Fiji)

Author keywords

Fuzzy finite state automata; Knowledge extraction; Recurrent neural network; Trakhtenbrot Barzdin algorithm

Indexed keywords

ARTIFICIAL INTELLIGENCE; EXTRACTIVE METALLURGY; FINITE AUTOMATA; FUZZY NEURAL NETWORKS; INTELLIGENT CONTROL; LEARNING SYSTEMS; NEURAL NETWORKS; REINFORCEMENT LEARNING; ROBOTS; TRANSLATION (LANGUAGES); VEGETATION;

DETERMINISTIC EQUIVALENTS; FINITE STATE AUTOMATONS; FUZZY FINITE-STATE AUTOMATA; FUZZY MEMBERSHIPS; GENERALISATION; KNOWLEDGE EXTRACTION; MACHINE LEARNINGS; TRAINED NETWORKS; TRAINING SETS; TRAKHTENBROT-BARZDIN ALGORITHM;

RECURRENT NEURAL NETWORKS;

EID: 56349143019     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. A.J Robinson, An application of recurrent nets to phone probability estimation, IEEE transactions on Neural Networks, 5(2), 1994, 298-305.
2. C.L. Giles, S. Lawrence & A.C. Tsoi, Rule inference for financial prediction using recurrent neural networks, Proc. of the IEEE/IAFE Computational Intelligence for Financial Engineering, New York City, USA, 1997, 253-259
3. K. Marakami & H Taguchi, Gesture recognition using recurrent neural networks, Proc. of the SIGCHI conference on Human factors in computing systems: Reaching through technology, Louisiana, USA, 1991, 237-242.
4. C. Lee Giles, C.W Omlin & K. Thornber, Equivalence in Knowledge Representation: Automata, Recurrent Neural Networks, and dynamical Systems, Proc. of the IEEE, 87(9), 1999, 1623-1640
5. J. L. Elman, Finding structure in time, Cognitive Science, 14, 1990, 179-211.
6. R. L. Watrous & G. M. Kuhn, Induction of finite-state languages using second-order recurrent networks, Proc. of Advances in Neural Information Systems, California, USA, 1992, 309-316.
7. T. Lin, B.G. Horne, P. Tino, & C.L. Giles, Learning long-term dependencies in NARX recurrent neural networks. IEEE Transactions on Neural Networks, 7(6), 1996, 1329-1338.
8. S. Hochreiter & J. Schmidhuber, Long short-term memory, Neural Computation, 9(8), 1997, 1735-1780.
9. E. B. Kosmatopoulos and M. A. Christodoulou, Neural networks for identification of fuzzy dynamical systems: An application to identification of vehicle highway systems, Proc. of the 4th IEEE Mediterranean Symposium, New Directions in Control and Automation, 1996, 23-38.
10. F. E. Cellier & Y. D. Pan, Fuzzy adaptive recurrent counter propagation neural networks: A tool for efficient implementation of qualitative models of dynamic processes, J. Syst. Eng., 5(4), 1995, 207-222.
11. E. B. Kosmatopoulos & M. A. Christodoulou, Structural properties of gradient recurrent high-order neural networks, IEEE Trans. Circuits Syst., 42, 1995, 592-603.
12. E. B. Kosmatopoulos, M. M. Polycarpou, M. A. Christodoulou, & P. A. Ioannou, High-order neural networks for identification of dynamical systems, IEEE Trans. Neural Networks, 6, 1995, 422-431.
13. E. B. Kosmatopoulos and M. A. Christodoulou, Recurrent neural networks for approximation of fuzzy dynamical systems, Int. J. Intell. Control Syst., 1(2), 1996, 223-233.
14. C. W. Omlin, K. K. Thornber, & C. L. Giles, Fuzzy finite state automata can be deterministically encoded into recurrent neural networks, IEEE Trans. Fuzzy Syst., 6, 1998, 76-89.
15. H. Jacobsson, Rule extraction from recurrent neural networks: A taxonomy and review, Neural Computation, 17 (6), 2005,1223-1263.
16. S. Das & R. Das, Induction of discrete state-machine by stabalising a continuous recurrent neural network using clustering. Journal of Computer Science and Informatics, 21(2), 1991, 35-40.
17. A. Vahed & C. W Omlin, Rule extraction from recurrent neural networks using a symbolic machine learning algorithm, Proc. of the 6th International Conference on Neural Information Processing, Dunedin, New Zealand, 1999, 712-717.
18. D. Dubois & H. Prade, Fuzzy sets and systems: theory and applications, Mathematics in science and engineering, 14, 1980, 220-226.
19. C. Omlin & C. L. Giles, Extraction of rules from discrete time recurrent neural networks, Journal of the ACM, 43(6), 1996, 937-972.
20. B. Trakhenbrot & Y. Barzdin, Finite automata: Behaviour and synthesis, North- Holland, Amsterdam, 1973.