A complex-valued neuro-fuzzy inference system and its learning mechanism

Neurocomputing

Volumn 123, Issue , 2014, Pages 110-120

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

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

Complex valued neural network; Fuzzy inference system; Meta cognition; Self regulation; Wirtinger calculus

Indexed keywords

COMPLEX-VALUED NEURAL NETWORKS; FUZZY INFERENCE SYSTEMS; META COGNITIONS; SELF-REGULATION; WIRTINGER CALCULUS;

BENCHMARKING; CALCULATIONS; COGNITIVE SYSTEMS; FUZZY SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS;

APPROXIMATION ALGORITHMS;

ARTICLE; AUTOREGULATION; CLASSIFICATION ALGORITHM; COGNITION; COMPLEX VALUED NEURO FUZZY INFERENCE SYSTEM; CONCEPTUAL FRAMEWORK; DECISION MAKING; FUZZY SYSTEM; LEARNING ALGORITHM; MACHINE LEARNING; NORMAL DISTRIBUTION; PREDICTION; PRIORITY JOURNAL; PROBLEM SOLVING;

EID: 84885861235     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.06.009     Document Type: Article

References (56)

1. Prabha N.R., Marimuthu N.S., Babulal C.K. Adaptive neuro-fuzzy inference system based total demand distortion factor for power quality evaluation. Neurocomputing 2009, 73(1-3):315-323.
2. Lim J.S., Wang D., Kim Y.-S., Gupta S. A neuro-fuzzy approach for diagnosis of antibody deficiency syndrome. Neurocomputing 2006, 69(7-9):969-974.
3. Bregains J., Ares F. Analysis, synthesis and diagnostics of antenna arrays through complex-valued neural networks. Microwave and Optical Technology Letters 2006, 48(8):1512-1515.
4. Li M., Huang G., Saratchandran P., Sundararajan N. Complex valued growing and pruning RBF neural networks for communication channel equalization. IEEE Proceedings of Vision, Image and Signal Processing 2006, 153(4):411-418.
5. Aizenberg I., Paliy D., Zurada J., Astola J. Blur identification by multilayer neural network based on multivalued neurons. IEEE Transactions on Neural Networks 2008, 19(5):883-898.
6. Muezzinoglu M., Guzelis C., Zuruda J. A new design method for the complex valued multistate hopfield associative memory. IEEE Transactions on Neural Networks 2003, 14(4):891-899.
7. Mandic D., Javidi S., Goh S., Kuh A., Aihara K. Complex valued prediction of wind profile using augmented complex statistics. Renewable Energy 2009, 34(1):196-201.
8. A. Ghani, M. Amin, K. Murase, Real time hand gesture recognition using complex valued neural network (CVNN), in: Neural Information Processing, Lecture Notes in Computer Science, 2011, pp. 541-549.
9. T. Nitta, The computational power of complex-valued neuron, in: Artificial Neural Networks and Neural Information Processing ICANN/ICONIP, Lecture Notes in Computer Science, vol. 2714, 2003, pp. 993-1000.
10. Nitta T. An extension of back-propagation algorithm to complex numbers. Neural Networks 1997, 48(1):180-192.
11. Kim T., Adali T. Approximation by fully complex multilayer perceptron. Neural Computation 2003, 15(7):1641-1666.
12. Savitha R., Suresh S., Sundararajan N. A fully complex-valued radial basis function network and its learning algorithm. International Journal of Neural Systems 2009, 19(4):253-267.
13. Chen S., McLaughlin S., Mulgrew N. Complex valued radial basis function network, part I. network architecture and learning algorithms. EURASIP Signal Processing Journal 1994, 35(1):19-31.
14. Wirtinger W. Zur formalen theorie der funktionen von mehr komplexen vernderlichen. Annals of Mathematics 1927, 97:357-375.
15. Li H., Adali T. Complex-valued adaptive signal processing using nonlinear functions. EURASIP Journal on Advances in Signal Processing 2008 2008, 765615:1-9.
16. Savitha R., Suresh S., Sundararajan N. Metacognitive learning in a fully complex-valued radial basis function neural network. Neural Computation 2012, 24(5):1297-1328.
17. R. Savitha, S. Suresh, N. Sundararajan, Complex-valued function approximation using a fully complex-valued RBF (FC-RBF) learning algorithm, in: Proceedings of International Joint Conference on Neural Networks, 2009, pp. 2819-2825.
18. Amin M.F., Savitha R., Amin M.I., Murase K. Orthogonal least squares based complex-valued functional link network. Neural Networks 2012, 32:257-266. (Special Issue).
19. Subramanian K., Suresh S. A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system. Applied Soft Computing 2012, 12(11):3603-3614.
20. Rong H.-J., Sundararajan N., Huang G.-B., Saratchandran P. Sequential adaptive fuzzy inference system (SAFIS) for nonlinear system identification and prediction. Fuzzy Sets and Systems 2006, 157(9):1260-1275.
21. D.P. Joysula, H. Vadali, J. Donahue, F. Hughes, Modeling meta-cognition for learning in artificial systems, in: World Congress on Nature and Biologically Inspired Computing, 2009, pp. 1419-1424.
22. Flavell J. Meta-cognition and cognitive monitoring. a new area of cognitive-developmental inquiry. American Psychologist 1979, 34(10):906-911.
23. H. Wellman, Metamemory revisited, in: M.T.H. Chi (Ed.), Contributions to Human Development, vol. 9, Trends in Memory Development Research, 1983, pp. 31-51.
24. Perlovsky L. Neural mechanisms of the mind, Aristotle, Zadeh and fMRI. IEEE Transactions on Neual Networks 2010, 21(5):718-733.
25. Nelson T.-O., Narens L. Metamemory. a theoretical framework and new findings. Psychology of Learning and Motication 1990, 26(C):125-173.
26. Flavell J. Cognitive Development 1988, Prentice Hall, Great Britain.
27. K. Subramanian, S. Sundaram, N. Sundararajan, A meta-cognitive neuro-fuzzy inference system (McFIS) for sequential classification problems, IEEE Transactions on Fuzzy Systems, doi:. doi:10.1109/TFUZZ.2013.2242894.
28. Suresh S., Dong K., Kim H. A sequential learning algorithm for self adaptive resource allocation network classifier. Neurocomputing 2010, 73(16-18):3012-3019.
29. Suresh S., Savitha R., Sundararajan N. A sequential learning algorithm for complex valued self regulating resource allocation network-CSRAN. IEEE Transactions on Neural Networks 2011, 22(7):1061-1072.
30. Sateesh Babu G., Suresh S. Metacognitive neural network for classification problems in a sequential learning framework. Neurocomputing 2011, 81(1):86-96.
31. Savitha R., Suresh S., Sundararajan N. A meta-cognitive learning algorithm for a fully complex-valued relaxation network. Neural Networks 2012, 32:209-218. (Special Issue).
32. S. Suresh, K. Subramanian, A sequential learning algorithm for meta-cognitive neuro-fuzzy inference system for classification problems, in: Proceedings of International Joint Conference on Neural Networks, 2011, pp. 2507-2512.
33. Blake C., Merz C. UCI Repository of Machine Learning Databases 1998, Department of Information and Computer Sciences, University of California, Irvine, http://archive.ics.uci.edu/ml/.
34. Jianping D., Sundararajan N., Saratchandran P. Complex valued minimal resource allocation network for nonlinear signal processing. International Journal of Neural Systems 2000, 10(2):95-106.
35. Benvenuto N., Piazza F. On the complex backpropagation algorithm. IEEE Transactions on Signal Processing 1992, 40(4):967-969.
36. Haykins S. Neural Networks. A Comprehensive Foundation 1999, Prentice Hall, Upper Saddle River, NJ.
37. Remmert R. Theory of Complex Functions 1991, Springer Verlag, Berlin.
38. Savitha R., Suresh S., Sundararajan N. A new learning algorithm with logarithmic performance index for complex-valued neural networks. Neurocomputing 2009, 72(16-18):3771-3781.
39. R. Savitha, S. Suresh, N. Sundararajan, A projection based fast learning fully complex-valued relaxation neural network, in: IEEE Transactions on Neural Networks, vol. 24, 2013, pp. 529-541.
40. Li M., Huang G., Saratchandran P., Sundararajan N. Fully complex extreme learning machine. Neurocomputing 2005, 68:306-314.
41. Nitta T. Orthogonality of decision boundaries of complex-valued neural networks. Neural Computation 2004, 16(1):73-97.
42. Savitha R., Suresh S., Sundararajan N., Kim H. A fully complex valued radial basis function classifier for real valued classification problems. Neurocomputing 2012, 78(1):104-110.
43. Aizenberg I., Moraga C. Multilayer feedforward neural network based on multi-valued neurons MLMVN and a backpropagation learning algorithm. Soft Computing 2007, 11(2):169-193.
44. Amin M.F., Murase K. Single-layer complex-valued neural network for real-valued classification problems. Neurocomputing 2009, 72(4-6):945-955.
45. R. Savitha, S. Suresh, N. Sundararajan, H.J. Kim, Fast learning fully complex-valued classifiers for real-valued classification problems, in: Lecture Notes in Computer Science: Advances in Neural Networks, vol. 6675, 2011, pp. 602-609.
46. Savitha R., Suresh S., Sundararajan N. Fast learning circular complex-valued extreme learning machine (CC-ELM) for real-valued classification problems. Information Sciences 2012, 187:277-290.
47. Takagi T., Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics 1985, 15(1):116-132.
48. Suksmono A., Hirose A. Intelligent beam-forming by using a complex-valued neural network. Journal of Intelligent & Fuzzy Systems 2004, 15(3-4):139-147.
49. Savitha R., Suresh S., Sundararajan N. A new learning algorithm with logarithmic performance index for complex-valued neural networks. Neurocomputing 2009, 72(16-18):3771-3781.
50. Monzingo R.A., Miller T.W. Introduction to Adaptive Arrays 2003, SciTech Publishing, Raleigh, NC.
51. Patra J., Ral R., Baliarsingh R., Panda G. Nonlinear channel equalization for QAM signal constellation using artificial neural networks. IEEE Transactions on Systems, Man, and Cybernetics, Part B. Cybernetics 1999, 29(2):262-271.
52. Suresh S., Sundararajan N., Saratchandran P. Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences 2008, 178:2621-2638.
53. Zhang T. Statistical behavior and consistency of classification methods based on convex risk minimization. Annals of Statistics 2003, 32(1):56-85.
54. Cortes C., Vapnik V. Support-vector networks. Machine Learning 1995, 20(3):272-297.
55. Suresh S., Venkatesh Babu R., Kim H.J. No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing 2009, 9(2):541-552.
56. Demsar J. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 2006, 7(1):1-30.