Metacognitive learning in a fully complex-valued radial basis function neural network

Neural Computation

Volumn 24, Issue 5, 2012, Pages 1297-1328

  Savitha, R ^a   Suresh, S ^a   Sundararajan, N ^b  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^b NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; HUMAN; LEARNING; LETTER; NERVE CELL; PHYSIOLOGY; SIGNAL PROCESSING;

ALGORITHMS; HUMANS; LEARNING; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); NEURONS; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84860235672     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00254     Document Type: Letter

References (45)

1. Aizenberg, I., & Moraga, C. (2007). Multilayer feedforward neural network based on multi-valued neurons (MLMVN) and a backpropagation learning algorithm. Soft Computing, 11(2), 169-193.
2. Amin, M. F., Islam, M. M., & Murase, K. (2009). Ensemble of single-layered complexvalued neural networks for classification tasks. Neurocomputing, 72(10-12), 2227-2234.
3. Blake, C., & Merz, C. (1998). UCI repository of machine learning databases. Irvine: Department of Information and Computer Sciences, University of California, Irvine. http://archive.ics.uci.edu/ml/.
4. Brandwood, D. H. (1983). A complex gradient operator and its application in adaptive array theory. IEE Proceedings F: Radar and Signal Processing, 130(1), 11-16.
5. Bregains, J. C., & Ares, F. (2006). Analysis, synthesis, and diagnostics of antenna arrays through complex-valued neural networks. Microwave and Optical Technology Letters, 48(8), 1512-1515.
6. Cha, I., & Kassam, S. A. (1995). Channel equalization using adaptive complex radial basis function networks. IEEE Journal on Selected Areas in Communications, 13(1), 122-131.
7. Chen, S., McLaughlin, S., & Mulgrew, N. (1994). Complex-valued radial basis function network, part I: Network architecture and learning algorithms. EURASIP Signal Processing Journal, 35(1), 19-31.
8. Fiori, S. (2005). Nonlinear complex-valued extensions ofHebbian learning:An essay. Neural Computation, 17(4), 779-838.
9. Goh, S. L., & Mandic, D. P. (2004). A complex-valued RTRL algorithm for recurrent neural networks. Neural Computation, 16(12), 2699-2713.
10. Goh, S. L., & Mandic, D. P. (2007a). An augmented extended Kalman filter algorithm for complex-valued recurrent neural networks. Neural Computation, 19(4), 1039-1055.
11. Goh, S. L., & Mandic, D. P. (2007b). An augmented CRTRL for complex-valued recurrent neural networks. Neural Networks, 20(10), 1061-1066.
12. Haykins, S. (1999). Neural networks: A comprehensive foundation. Upper Saddle River, NJ: Prentice Hall.
13. Isaacson, R., & Fujita, F. (2006). Metacognitive knowledge monitoring and selfregulated learning: Academic success and reflections on learning. Journal of the Scholarship of Teaching and Learning, 6(1), 39-55.
14. Jianping, D., Sundararajan, N.,&Saratchandran, P. (2000). Complex-valued minimal resource allocation network for nonlinear signal processing. International Journal of Neural Systems, 10(2), 95-106.
15. Joysula, D. P., Vadali, H., Donahue, B. J., & Hughes, F. C. (2009). Modeling metacognition for learning in artificial systems. In Proceedings of 2009 World Congress on Nature and Biologically Inspired Computing (NABIC 2009) (pp. 1419-1424). Piscataway, NJ: IEEE.
16. Kim, T., & Adali, T. (2002). Fully-complex multilayer perceptron network for nonlinear signal processing. Journal of VLSI, Signal Processing, 32(1-2), 29-43.
17. Kim, T., & Adali, T. (2003). Approximation by fully complex multilayer perceptrons. Neural Computation, 15(7), 1641-1666.
18. Li, M. -B., Huang, G. -B., Saratchandran, P.,&Sundararajan, N. (2005). Fully complex extreme learning machines. Neurocomputing, 68(1-4), 306-314.
19. Li, M.-B., Huang, G.-B., Saratchandran, P., & Sundararajan, N. (2006). Complexvalued growing and pruning RBF neural networks for communication channel equalization. IEE Proc.-Vision, Image and Signal Processing, 153(4), 411-418.
20. Monzingo, R. A., & Miller, T. W. (2003). Introduction to adaptive arrays, Raleigh, NC: SciTech Publishing.
21. Nelson, T. O., & Narens, L. (1992). Metamemory: A theoretical framework and new findings. In T. O. Nelson (Ed.), Metacognition: Core readings (pp. 9-24). Needham Heights, MA: Allyn and Bacon.
22. Nitta, T. (2003). Solving the XOR problem and the detection of symmetry using a single complex-valued neuron. Neural Networks, 16(8), 1101-1105.
23. Nitta, T. (2004). Orthogonality of decision boundaries of complex-valued neural networks. Neural Computation, 16(1), 73-97.
24. Omkar, S. N., Suresh, S., Raghavendra, T. R., & Mani, V. (2002). Acoustic emission signal classification using fuzzy C-means clustering. In Proc. of the ICONIP'02, 9th International Conference on Neural Information Processing, 4 (pp. 1827-1831). Piscataway, NJ: IEEE.
25. Rattan, S.S.P., & Hsieh, W.W. (2005). Complex-valued neural networks for nonlinear complex principal component analysis. Neural Networks, 18(1), 61-69.
26. Remmert, R. (1991). Theory of complex functions. Berlin: Springer Verlag.
27. Rivers, W. P. (2001). Autonomy at all costs: An ethnography of metacognitive selfassessment and self-management among experienced language learners. Modern Language Journal, 85(2), 279-290.
28. Savitha, R., Suresh, S., & Sundararajan, N. (2009). A fully complex-valued radial basis function network and its learning algorithm. International Journal of Neural Systems, 19(4), 253-267.
29. Savitha, R., Suresh, S., & Sundararajan, N. (2010). A self-regulated learning in fully complex-valued radial basis function networks. In Proceedings of the International Joint Conference on Neural Networks (pp. 1-8). Piscataway, NJ: IEEE.
30. Savitha, R., Suresh, S., Sundararajan, N., & Kim, H. J. (2012). A fully complexvalued radial basis function classifier for real-valued classification problems. Neurocomputing, 78(1), 104-110.
31. Savitha, R., Suresh, S., Sundararajan, N., & Saratchandran, P. (2009). A new learning algorithm with logarithmic performance index for complex-valued neural networks. Neurocomputing, 72(16-18), 3771-3781.
32. Savitha, R., Vigneshwaran, S., Suresh, S., & Sundararajan, N. (2009). Adaptive beamforming using complex-valued radial basis function neural networks. Proceedings of the IEEE Region 10 Conference (pp. 1-6). Piscataway, NJ: IEEE.
33. Shen, C., Lajos, H., & Tan, S. (2008). Symmetric complex-calued RBF receiver for multiple-antenna-aided wireless systems. IEEE Trans. on Neural Networks, 19(9), 1659-1665.
34. Sinha, N., Saranathan, M., Ramakrishna, K. R., & Suresh, S. (2007). Parallelmagnetic resonance imaging using neural networks. IEEE International Conference on Image Processing, 3, 149-152.
35. Suksmono, A. B., & Hirose, A. (2004). Intelligent beamforming by using a complexvalued neural network. Journal of Intelligent and Fuzzy Systems, 15(3-4), 139-147.
36. Suresh, S., Keming, Dong, & Kim, H. J. (2010). A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing, 73(16-18), 3012-3019.
37. Suresh, S., Omkar, S. N., Mani, V., & Guru Prakash, N. (2003). Lift coefficient prediction at high angle of attack using recurrent neural network. Aerospace Science and Technology, 7(8), 595-602.
38. Suresh, S., Omkar, S. N., Mani, V., & Menaka, C. (2004). Classification of acoustic emission signal using genetic programming. Journal of Aerospace Science and Technology, 56(1), 26-41.
39. Suresh, S., Savitha, R., & Sundararajan, N. (2011). A sequential learning algorithm for complex-valued self-regulating resource allocation network-CSRAN. IEEE Transactions on Neural Networks, 22(7), 1061-1072.
40. Suresh, S., Sundararajan, N., & Saratchandran, P. (2008a). A sequential multicategory classifier using radial basis function networks. Neurocomputing, 71(7-9), 1345-1358.
41. Suresh, S., Sundararajan, N., & Saratchandran, P. (2008b). Risk-sensitive loss functions for sparse multi-category classification problems. Information Sciences, 178(12), 2621-2638.
42. Suresh, S., Venkatesh Babu, R., & Kim, H. J. (2009). No-reference image quality assessment using modified extreme learning machine classifier. Applied Soft Computing, 9(2), 541-552.
43. Wenden, A. L. (1998). metacognitive knowledge and language learning. Applied Linguistics, 19(4), 515-537.
44. Wirtinger, W. (1927). Zur formalen theorie der funktionen von mehr komplexen veränderlichen. Annals of Mathematics, 97.
45. Zhang, T. (2003). Statistical behavior and consistency of classificationmethods based on convex risk minimization. Ann. Stat., 32(1), 56-85.