A hybrid ART-GRNN online learning neural network with a ε-insensitive loss function

IEEE Transactions on Neural Networks

Volumn 19, Issue 9, 2008, Pages 1641-1646

  Yap, Keem Siah ^a   Lim, Chee Peng ^b   Abidin, Izham Zainal ^a  

^a Universiti Tenaga Nasional   (Malaysia)

^b SCHOOL OF PHYSICS   (Malaysia)

Author keywords

Adaptive resonance theory (ART); Bayesian theorem; Generalized regression neural network (GRNN); Online sequential extreme learning machine

Indexed keywords

ARTS COMPUTING; E-LEARNING; EDUCATION; FEEDFORWARD NEURAL NETWORKS; FOOD PROCESSING; IMAGE CLASSIFICATION; INTERNET; LEARNING SYSTEMS; RADIAL BASIS FUNCTION NETWORKS; RESONANCE; TIME SERIES ANALYSIS;

ADAPTIVE RESONANCE THEORIES; ADAPTIVE RESONANCE THEORY; ADAPTIVE RESONANCE THEORY (ART); BAYESIAN THEOREM; EMPIRICAL STUDIES; EXTREME LEARNING MACHINE; GAUSSIAN; GENERALIZED REGRESSION NEURAL NETWORK; GENERALIZED REGRESSION NEURAL NETWORK (GRNN); HYBRID MODEL; LOSS FUNCTIONS; NEURAL NETWORK MODELLING; ON-LINE LEARNING; ONLINE SEQUENTIAL EXTREME LEARNING MACHINE; RADIAL BASIS FUNCTION NEURAL NETWORKS; SEQUENTIAL LEARNING; TIME-SERIES PREDICTION;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; METHODOLOGY; ONLINE SYSTEM; THEORETICAL MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER); ONLINE SYSTEMS; PATTERN RECOGNITION, AUTOMATED;

EID: 52149111368     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2008.2000992     Document Type: Article

References (31)

1. C. M. Bishop, Neural Networks for Pattern Recognition. Oxford, U.K.: Clarendon, 1995.
2. D. E. Rumellhart, G. E. Hinton, and R. J. Williams, "Learning internal representations by error propagation," Parallel Distribution Process: Explanations Microstructure of Cognition, vol. 1, pp. 318-362, 1986.
3. S. Chen, C. F. N. Cowan, and P. M. Grant, "Orthogonal least squares learning algorithm for radial basis function networks," IEEE Trans. Neural Netw., vol. 2, no. 2, pp. 3 02-3 09, Mar. 1991.
4. J. Platt, "A resource-allocating network for function interpolation," Neural Comput., vol. 3, pp. 213-225, 1991.
5. V. Kadirkamanathan and M. Niranjan, "A function estimation approach to sequential learning with neural networks," Neural Comput., vol. 5, pp. 954-975, 1993.
6. L. Yingwei, N. Sundararajan, and P. Saratchandran, "Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm," IEEE Trans. Neural Netw., vol. 9, no. 2, pp. 308-318, Mar. 1998.
7. G.-B. Huang, P. Saratchandran, and N. Sundararajan, "An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks," IEEE Trans. Syst. Man Cybern. B, Cybern., vol. 34, no. 6, pp. 2284-2292, Nov. 2004.
8. G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation," IEEE Trans. Neural Netw., vol. 16, no. 1, pp. 57-67, Jan. 2005.
9. N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, "A fast and accurate online sequential learning algorithms for feedforward network," IEEE Trans. Neural Netw., vol. 17, no. 6, pp. 1411-1423, Nov. 2006.
10. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, "Real-time learning capability of neural networks," IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 863-878, Jul. 2006.
11. J. R. Williamson, "Gaussian ARTMAP: A neural network for fast incremental learning of noisy multidimensional maps," Neural Netw. vol. 9, no. 5, pp. 881-897, 1996.
12. D. F. Specht, "A general regression neural network," IEEE Trans. Neural Netw., vol. 2, no. 6, pp. 568-576, Nov. 1991.
13. E. Parzen, "On estimation of a probability density function and mode," Ann. Math. Statist., vol. 33, pp. 1065-1076, 1962.
14. G. A. Carpenter, S. Grossberg, and D. B. Rosen, "Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system," Neural Netw., vol. 4, pp. 759-771, 1991.
15. G. A. Carpenter, S. Grossberg, N. Markuzon, and J. H. Reynolds, "Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps," IEEE Trans. Neural Netw. vol. 3, no. 5, pp. 698-713, Sep. 1992.
16. K. S. Yap, C. P. Lim, I. Zainal Abidin, and M. Malik, "A hybrid neural network for time series prediction," in Proc. 3rd Int. Conf. Artif. Intell. Eng. Technol., 2006, vol. 1, pp. 451-456.
17. K. S. Yap, I. Zainal Abidin, and C. P. Lim, "Short term load forecasting using a hybrid neural network," in Proc. IEEE 1st Int. Conf. Power Energy, 2006, vol. 1, pp. 123-128.
18. W. Chu, S. S. Keerthi, and C. J. Ong, "Bayesian support vector regression using a unified loss function," IEEE Trans. Neural Netw. vol. 15, no. 1, pp. 29-44, Jan. 2004.
19. J. Xu and X.-G. Zhang, "Kernels based on weighted Levenshtein distance," in Proc. Int. Joint Conf. Neural Netw., Budapest, Hungary, Jul. 25-29, 2004, vol. 4, pp. 3015-3018.
20. B. Efron, "Bootstrap methods: Another look at the jackknife," Ann. Statist., vol. 7, pp. 1-26, 1979.
21. B. Efron, "Nonparametric standard errors and confidence intervals," Can. J. Statist., vol. 9, pp. 139-172, 1981.
22. E. W. M. Lee, C. P. Lim, R. K. K. Yuen, and S. M. Lo, "A hybrid neural network model for noisy data regression," IEEE Trans. Syst. Man Cybern. B, Cybern., vol. 34, no. 2, pp. 951-960, Apr. 2004.
23. R. Andonie and L. Sasu, "Fuzzy ARTMAP with input relevance," IEEE Trans. Neural Netw., vol. 17, no. 4, pp. 929-941, Jul. 2006.
24. J. Leng, L. Jain, and C. Fyfe, "Simulation and reinforcement learning with soccer agents," J. Multi-Agent Grid Syst., vol. 4, no. 4, Dec. 2008, to be published.
25. B. Moore, "ART 1 and pattern clustering," in Proc. Connectionist Models Summer School, San Mateo, CA, 1988, pp. 174-185.
26. L. Mah and K. Khorasi, "Constructive feedforward neural networks using hermite polynomial activation functions," IEEE Trans. Neural Netw. vol. 16, no. 4, pp. 821-833, Jul. 2005.
27. N. Srinivasa, "Learning and generalization of noisy mappings using a modified PROBART neural network," IEEE Trans. Signal Process., vol. 45, no. 10, pp. 2533-2550, Oct. 1997.
28. S. Marriott and R. S. Harrison, "A modified fuzzy ARTMAP architecture for the approximation of noisy mappings," Neural Netw., vol. 8, no. 4, pp. 619-641, 1995.
29. E. Gomez-Sanchez, Y. A. Dimitriadis, J. M. Cano-Izquierdo, and J. Lopez-Coronado, "μ ARTMAP: Use of mutual information for category reduction in fuzzy ARTMAP" IEEE Trans. Neural Netw., vol. 13, no. 1, pp. 58-69, Jan. 2002.
30. M. Ankerst, M. Ester, and H. P. Kriegel, "Towards an effective cooperation of the user and the computer for classification," in Proc. 6th ACM SIGKDD Int. Conf. Knowl. Disc. Data Mining, 2000, pp. 179-189.
31. The Laboratory of Artificial Intelligence and Decision Support, University of Porto, "DNA dataset description," Porto, Portugal, Jan. 2006 [Online]. Available: http://www.liaad.up.pt/ old/statlog/datasets/dna/dna.descri.html