Decompositional rule extraction from artificial neural networks and application in analysis of transformers

2009 15th International Conference on Intelligent System Applications to Power Systems, ISAP '09

Volumn , Issue , 2009, Pages

  Amora, M A B ^a   Almeida, O M ^a   Braga, A P S ^a   Barbosa, F R ^a   Lima, S S ^a   Lisboa, L A C ^b  

^a FEDERAL UNIVERSITY OF CEARÁ   (Brazil)

^b Divis o De Projetos De Fontes Alternativas   (Brazil)

Author keywords

Fuzzy rule based systems; Knowledge rule extraction; Neural networks; Transformer failure diagnosis

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BLACK BOXES; COMPUTATIONAL MODEL; FUZZY RULE-BASED SYSTEMS; INCIPIENT FAULTS; KNOWLEDGE RULE EXTRACTION; KNOWLEDGE RULES; RULE EXTRACTION; TRANSFORMER FAILURE DIAGNOSIS; TRANSFORMER OIL;

DISSOLUTION; FAILURE ANALYSIS; FUZZY RULES; INTELLIGENT SYSTEMS; OIL FILLED TRANSFORMERS;

FUZZY NEURAL NETWORKS;

EID: 76549104306     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISAP.2009.5352932     Document Type: Conference Paper

References (23)

1. J. M. Benitez, J. L. Castro, and I. Requena, "Are artificial neural networks black boxes?", IEEE Trans. Neural Netw., vol. 8, no. 5, pp. 1156-1164, Sep. 1997.
2. J. L. Castro, C. J. Mantas, and J. M. Benitez, "Interpretation of artificial neural networks by means of fuzzy rules", IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 101-116, Jan. 2002.
3. G. Bologna, "A model for single and multiple knowledge based networks", ELSEVIER Artificial Intelligence in Medicine no. 28, pp. 141-163, 2003.
4. J. Huysmans, B. Baesens and J. Vanthienen. "Using rule extraction to improve the comprehensibility of predictive models". Katholieke Universiteit Leuven. Department of Decision Sciences and Information Management. Leuven, Belgium, 2006.
5. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators" Neural Networks, vol. 2, pp. 359-366, 1989.
6. Lima S. E. U. "Diagnóstico Inteligente de Falhas Incipientes em Transformadores de Potência Utilizando Análise dos Gases Dissolvidos em Óleo", Dissertação de Mestrado, UFC/CT/DEE, Fortaleza-CE, 2005.
7. M.W. Craven and J.W. Shavlik. "Extracting tree-structured representations of trained networks". In David S. Touretzky, Michael C. Mozer and Michael E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 24-30. The MIT Press, 1996.
8. U. Johansson, R. König and L. Niklasson. "Rule extraction from trained neural networks using genetic programming". In Joint 13th International Conference on Artificial Neural Networks and 10th International Conference on Neural Information Processing, ICANN/ICONIP 2003, pages 13-16, 2003.
9. I. Taha and J. Ghosh. "Symbolic interpretation of artificial neural networks". IEEE Transactions on Knowledge and Data Engineering, 11(3):448-463, 1999.
10. Z.-H. Zhou, S.-F. Chen and Z.-Q. Chen. "A statistics based approach for extracting priority rules from trained neural networks". Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 3, 2000.
11. Z.-H. Zhou, Y. Jiang and S.-F. Chen. "Extracting symbolic rules from trained neural network ensembles". AI Communications, 16(1):3-15, 2003.
12. U. Markowska-Kaczmar and M. Chumieja. "Discovering the mysteries of neural networks". International Journal of Hybrid Intelligent Systems, 1(3-4):153-163, 2004.
13. F. Chen. "Learning accurate and understandable rules from SVM classifiers". Master's thesis, Simon Fraser University, 2004.
14. G.P.J. Schmitz, C. Aldrich and F.S. Gouws. "ANN-DT: An algorithm for extraction of decision trees from artificial neural networks". IEEE Transactions on Neural Networks, 10(6):1392-1401, 1999.
15. N. Barakat and J. Diederich. "Eclectic rule-extraction from support vector Machines". International Journal of Computational Intelligence, 2(1):59-62, 2005.
16. G. Fung, S. Sandilya and R.B. Rao. "Rule extraction from linear support vector machines". In 11th ACM SIGKDD international conference on Knowledge discovery in data mining, pages 32-40, 2005.
17. R. Setiono and W.K. Leow. "FERNN: An algorithm for fast extraction of rules from neural networks". Applied Intelligence, 12(1-2):15-25, 2000.
18. R. Setiono and H. Liu. "Neurolinear: From neural networks to oblique decision rules". Neural Computing, 17(1):1-24, 1997.
19. R. Setiono and B. Baesens. "Risk management using recursive neural network rule extraction". Submitted to Management Science, 2006.
20. H. Núñez, C. Angulo and A. Català. "Rule extraction from support vector machines". In European Symposium on Artificial Neural Networks (ESANN), pages 107-112, 2002.
21. S. Thrun. "Extracting provably correct rules from artificial neural networks". Technical report iai-tr-93-5, Universität Bonn, Institut fur Informatik III, 1993.
22. R. Setiono, W.K. Leow and J.M. Zurada. "Extraction of rules from artificial neural networks for nonlinear regression". IEEE Transactions on Neural Networks, 13(3):564-577, 2002.
23. K. Saito and R. Nakano. "Extracting regression rules from neural networks". Neural Networks, 15(10):1279-1288, 2002.