A Meta-cognitive Interval Type-2 fuzzy inference system classifier and its projection based learning algorithm

Proceedings of the 2013 IEEE Conference on Evolving and Adaptive Intelligent Systems, EAIS 2013 - 2013 IEEE Symposium Series on Computational Intelligence, SSCI 2013

Volumn , Issue , 2013, Pages 48-55

  Subramanian, K ^a   Savitha, R ^a   Suresh, S ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

class specific error; classification; hinge loss; Interval type 2 neuro fuzzy system; meta cognition; self regulation

Indexed keywords

BENCHMARK CLASSIFICATION; HINGE-LOSS; META COGNITIONS; NEURO-FUZZY INFERENCE SYSTEMS; NEUROFUZZY SYSTEM; SELF-REGULATION; TAKAGI-SUGENO-KANG TYPES; UCI MACHINE LEARNING REPOSITORY;

CLASSIFICATION (OF INFORMATION); COGNITIVE SYSTEMS; FUZZY SYSTEMS; INTELLIGENT SYSTEMS; KNOWLEDGE REPRESENTATION; NETWORK LAYERS;

LEARNING ALGORITHMS;

EID: 84885226612     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/EAIS.2013.6604104     Document Type: Conference Paper

References (36)

1. K. Subramanian, R. Savitha, and S. Suresh, "Complex-valued neurofuzzy inference system for wind prediction," in Intl. Joint Conf. on Neural Net., 2012, pp. 1-7.
2. G. Zhang, H. Li, and M. Gan, "Design a wind speed prediction model using probabilistic fuzzy system," IEEE Trans. Industrial Informatics, vol. 8, no. 4, pp. 819-827, 2012.
3. K. Subramanian, S. Suresh, and R. Venkatesh Babu, "Meta-cognitive neuro-fuzzy inference system for human emotion recognition," in Intl. Joint Conf. on Neural Net., 2012, pp. 1-7.
4. H. Rong, N. Sundararajan, G. Huang, and P. Saratchandran, "Sequential adaptive fuzzy inference system SAFIS for nonlinear system identification and prediction," Fuzzy Sets and Syst., vol. 157, no. 9, pp. 1260-1275, 2006.
5. C. F. Juang, "A self-evolving interval type-2 fuzzy neural network with online structure and parameter learning," IEEE Trans. on Fuzzy Systems, vol. 16, no. 6, pp. 1411-1424, 2008.
6. -, "An interval type-2 fuzzy neural network with support vector regerssion for noisy regression problems," IEEE Trans. on Fuzzy Systems, vol. 18, no. 4, pp. 686-699, 2010.
7. R. Martinez, O. Castillo, and L. Aguilar, "Optimization of interval type-2 fuzzy logic controllers for a perturbedautonomous wheeled mobile robot using genetic algorithms," Information Sciences, vol. 179, no. 13, pp. 2158-2174, 2009.
8. J. C. Platt, "A resource allocating network for function interpolation," Neural Computation, vol. 3, no. 2, pp. 213-225, 1991.
9. N. Kasabov, "Evolving fuzzy neural networks for supervised/ unsupervised online knowledge-based learning," IEEE Trans. Syst., Man, Cybern., Part B: Cybern, vol. 31, no. 6, pp. 902-918, 2002.
10. Q. Song and N. Kasabov, "Dynamic evolving neuro-fuzzy inference system DENFIS: Online learning and application for time-series prediction," IEEE Trans. Fuzzy Syst., vol. 10, no. 2, pp. 144-154, 2002.
11. P. Angelov and P. Filev, "An approach to online identification of Takagi-Sugeno fuzzy models," IEEE Trans. Syst., Man and Cybern., Part B: Cybern., vol. 34, no. 1, pp. 484-498, 2004.
12. K. Subramanian and S. Suresh, "A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system," Applied Soft Computing, vol. 12, no. 11, pp. 3603-3614, 2012.
13. J. Mendel, "Advances in type-2 fuzzy sets and systems," Information Sciences, vol. 177, no. 1, pp. 84-110, 2007.
14. O. Castillo and P. Melin, "A review on the design and optimization of interval type-2 fuzzy controllers," Applied Soft Computing, vol. 12, no. 4, pp. 1267-1278, 2012.
15. J. Tavoosi and M. A. Badamchizadeh, "A class of type-2 fuzzy neural networks for nonlinear dynamical system identification," Neural Computation and Application, 2012, DOI: 10. 1007/s00521-012-0981-7.
16. J. Castro, O. Castillo, P. Melin, and A. Rodriguez-Diaz, "A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks," Information Sciences, vol. 179, no. 13, pp. 2175-2193, 2009.
17. R. H. Abiyev, O. Kaynak, T. Alshanableh, and F. Mamedov, "A type-2 neuro-fuzzy system based on clustering and gradient techniques applied to system identification and channel equalization," Applied Soft Computing, vol. 11, no. 1, pp. 1396-1406, 2011.
18. L. Lucas, T. Centeno, and M. Delgado, "General type-2 fuzzy inference systems: Analysis, design and computational aspects," in Fuzzy Systems Conference, 2007, pp. 1-6.
19. J. Mendel, "Type-2 fuzzy sets and systems: An overview," IEEE Computational Intelligence Magazine, vol. 2, no. 1, pp. 20-29, 2007.
20. Q. Liang and J. Mendel, "Interval type-2 fuzzy logic systems: Theory and design," IEEE Trans. on Fuzzy Systems, vol. 8, no. 5, pp. 535-550, 2000.
21. R. Isaacson and F. Fujita, "Metacognitive knowledge monitoring and self-regulated learning: Academic success and reflections on learning," Journal of the Scholarship of Teaching and Learning, vol. 6, no. 1, pp. 39-55, 2006.
22. S. Suresh, K. Dong, and H. Kim, "A sequential learning algorithm for self-adaptive resource allocation network classifier," Neurocomputing, vol. 73, no. 16-18, pp. 3012-3019, 2010.
23. G. Sateesh Babu and S. Suresh, "Meta-cognitive neural network for classification problems in a sequential learning framework," Neurocomputing, vol. 81, no. 1, pp. 86-96, 2012.
24. G. Babu and S. Suresh, "Meta-cognitive RBF network and its projection based learning algorithm for classification problems," Applied Soft Computing, vol. 13, no. 1, pp. 654-666, 2013.
25. G. Sateesh Babu and S. Suresh, "Parkinson's disease prediction using gene expression-A projection based learning meta-cognitive neural classifier approach," Expert Systems with Applications, vol. 40, no. 5, pp. 1519-1529, 2013.
26. G. Babu and S. Suresh, "Sequential projection based metacognitive learning in a radial basis function network for classification problems," IEEE Trans. Neural Networks and Learning Systems, vol. 24, no. 2, pp. 194-206, 2013.
27. R. Savitha, S. Suresh, and N. Sundararajan, "Metacognitive learning in a fully complex valued radial basis function neural network," Neural Computation, vol. 24, no. 5, pp. 1297-1328, 2012.
28. -, "A meta-cognitive learning algorithm for a fully complex-valued relaxation network," Neural Networks, vol. 32, no. Special Issue, pp. 209-218, 2012.
29. S. Suresh, R. Savitha, and N. Sundararajan, "A sequential learning algorithm for complex valued self regulating resource allocation network-CSRAN," IEEE Trans. Neural Networks, vol. 22, no. 7, pp. 1061-1072, 2011.
30. K. Subramanian and S. Suresh, "Human action recognition using metacognitive neuro-fuzzy inference system," International Journal of Neural Systems, vol. 22, no. 6, p. 1250028 (15), 2012.
31. T. O. Nelson and L. Narens, "Metamemory: A theoretical framework and new findings," psychology of Learning and Motivation, vol. 26, no. C, pp. 125-173, 1990.
32. T. Zhang, "Statistical behavior and consistency of classification methods based on convex risk minimization," Annals of Statistics, vol. 32, no. 1, pp. 56-85, 2003.
33. S. Suresh, N. Sundararajan, and P. Saratchandran, "Risk sensitive loss functions for sparse multi-category classification problems," Information Sciences, vol. 179, no. 21, pp. 2621-2638, 2008.
34. H. Hoffmann, "Kernel PCA for novelty detection," Pattern Recognition, vol. 40, no. 3, pp. 863-874, 2007.
35. C. Blake and C. Merz, "UCI repository of machine learning databases," 1998, department of Information and Computer Sciences, University of California, Irvine. [Online]. Available: Http://archive. ics. uci. edu/ml/
36. S. Suresh, R. Venkatesh Babu, and H. Kim, "No reference image quality assessment using modified extreme learning machine classifier," Applied Soft Computing, vol. 9, no. 2, pp. 541-552, 2009.