Learning algorithm of wavelet network based on sampling theory

Neurocomputing

Volumn 71, Issue 1-3, 2007, Pages 244-269

  Zhang, Zhiguo ^a  

^a JIANGSU AUTOMATION RESEARCH INSTITUTE   (China)

Author keywords

Convergence of algorithm; Overfitting; Wavelet neural networks

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; LEARNING ALGORITHMS; LOW PASS FILTERS; SPURIOUS SIGNAL NOISE; WAVELET TRANSFORMS;

BAND LIMITED FUNCTION; OVERFITTING; WAVELET NETWORK;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; LEARNING; MATHEMATICAL ANALYSIS; NOISE; PRIORITY JOURNAL; SAMPLING; SIMULATION; THEORY;

EID: 35648999424     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.01.001     Document Type: Article

References (39)

1. Akaike H. A new look at statistical model identification. IEEE Trans. Automat. Control AC-19 6 (1974) 716-723
2. Akifumi Y., Mamoru N., and Makio I. A note on sample size determination for Akaike Information Criterion (AIC) approach to clinical data analysis. Commun. Stat.-Theory Methods 34 12 (2005) 2331-2343
3. L. Beiwei, H. W. William, Simplified nonlinear principal component analysis, in: Proceedings of the International Joint Conference on Neural Networks, 2003, pp. 759-763.
4. P. Chandra, Y. Singh, Regularization and feedforward artificial neural network training with noise, in: Proceedings of the International Joint Conference on Neural Networks, 2003, pp. 2366-2371.
5. L. ChangGyoon, K. Kangchui, K. Eungkon, Modeling for an adaptive wavelet network parameter learning using genetic algorithms, in: Proceedings of the Fifteenth IASTED International Conference on Modeling and Simulation, CA, USA, 2004, pp. 55-59.
6. Chen-Chia C., Shun-Feng S., Jin-Tsong J., and Chih-Ching H. Robust support vector regression networks for function approximation with outliers. IEEE Trans. Neural Networks 13 6 (2002) 1322-1330
7. Chua C.G., and Goh A.T.C. A hybrid Bayesian back-propagation neural network approach to multivariate modeling. Int. J. Numer. Anal. Methods Geomech. 27 8 (2003) 651-667
8. Chui C.K. An Introduction to Wavelets (1992), Academic Press, Boston
9. Chunguang L., Peng W., and Javier E. Complex-valued wavelet network. J. Comput. System Sci. 67 3 (2003) 623-632
10. C. Daniel, O. Umit, Wavelet neural networks: a design perspective, in: IEEE International Symposium on Intelligent Control-Proceedings, 1994, pp. 376-381.
11. Daubechies I. Ten Lectures on Wavelets (1992), Capital City Press
12. M. David, Gaussian processes-a replacement for supervised neural networks 〈http://wol.ra.phy.cam.ac.uk/mackay/GP/〉, 1997.
13. Erhard R. Application of Bayesian trained RBF networks to nonlinear time-series modeling. Signal Process. 83 7 (2003) 1393-1410
14. Farokh M. Nonuniform Sampling: Theory and Practice (2001), Kluwer Academic/Plenum Publishers, Dordrecht/New York
15. Jarmo I., Joni-Kristian K., and Jouni L. Differential evolution training algorithm for feed-forward neural networks. Neural Process. Lett. 17 1 (2003) 93-105
16. X. Jinhua, W.C. Ho. Daniel, A constructive algorithm for wavelet neural networks, First International Conference on Natural Computation, Changsha, China, 2005, pp. 730-739.
17. Jun Z., Walter G.G., and Miao Y. Wavelet neural networks for function learning. IEEE Trans. Signal Process. 43 6 (1995) 1485-1496
18. C. Kai, H. Yoshio, An adaptive hybrid wavelet neural network and its application, in: Proceedings of the 2004 IEEE International Conference on Robotics and Biomimetics, IEEE ROBIO 2004, Shenyang, China, 2004, pp. 73-86.
19. Koji T., Masashi S., and Klaus-Robert M. Subspace information criterion for nonquadratic regularizers-model selection for sparse regressors. IEEE Trans. Neural Networks 13 1 (2002) 70-80
20. Mallat S. A Wavelet Tour of Signal Processing. second ed (2003), China Machine Press, Beijing
21. H. Min, C. Baotong, A novel learning algorithm for wavelet neural networks, in: First International Conference on Natural Computation, ICNC, Changsha, China, 2005, pp. 1-7.
22. Murata N., Yoshizawa S., and Amari S. Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Networks 5 6 (1994) 865-872
23. Nakashima A., Hirabayashi A., and Ogawa H. Error correcting memorization learning for noisy training examples. Neural Networks 14 1 (2001) 79-92
24. Qianhua Z., and Benvenist A. Wavelet network. IEEE Trans. Signal Process. 13 6 (1992) 889-898
25. Qinghua Z. Using wavelet network in nonparameters estimation. IEEE Trans. Neural Networks 8 (1997) 227-236
26. S. Qingmei, G. Yongchao, A stepwise updating algorithm for multiresolution wavelet neural networks, in: Proceedings of the International Conference on Wavelet Analysis and Its Applications (WAA), Chongqing, China, 2003, pp. 633-638.
27. Robert Y.M. An Introduction to Nonharmonic Fourier Series (1992), Academic Press, New York
28. K. Seong-Joo, K. Taek-Yong, S. Jae-yong, J. Hong-Tae, Design of the scaling-wavelet neural network using genetic algorithm, in: Proceedings of the International Joint Conference on Neural Networks, Honolulu, HI, 2002, pp. 2174-2179.
29. Thomas B., and Joseph C.E. An improved Akaike information criterion for state-space model selection. Comput. Stat. Data Anal. 50 10 (2006) 2635-2654
30. H.Q. Thuan, S. Rudy, Effective neural network pruning using cross-validation, in: Proceedings of the International Joint Conference on Neural Networks, 2005, pp. 972-977.
31. Tomaso P., and Federico G. Networks for approximation and learning. Proc. IEEE 78 (1990) 1481-1497
32. Van Der, Merwe N.T., and Hoffman A.J. Developing an efficient cross validation strategy to determine classifier performance (CVCP). Proc. Int. Jt. Conf. Neural Networks 3 (2001) 1663-1668
33. Vanessa R., Rafael R., and Antonio S. Learning hybrid Bayesian networks using mixtures of truncated exponentials. Int. J. Approx. Reason. 42 1-2 (2006) 54-68
34. C.E. Vasios, G.K. Matsopoulos, E.M. Ventouras, K.S. Nikita, N. Uzunoglu, Cross-validation and neural network architecture selection for the classification of intracranial current sources, in: 2004 Seventh Seminar on Neural Network Applications in Electrical Engineering-Proceedings, NEUREL 2004, pp. 151-158.
35. Yacine O., and Gerard D. Initialization by selection for wavelet network training. Neurocomputing 34 (2002) 131-143
36. J. Yaochu, O. Tatsuya, S. Bernhard, Neural network regularization and ensembling using multi-objective evolutionary algorithms, in: Proceedings of the 2004 Congress on Evolutionary Computation, CEC2004, 2004, pp. 1-8.
37. Yevqeniy B., Nataliya L., Iryna P., and Olena V. An adaptive learning algorithm for a wavelet neural network. Expert Systems 22 5 (2005) 235-240
38. Ying L., Zhidong D., and Xi Z. Approximated non-tensor pre-wavelets neural network based on compound training algorithm. J. Inform. Comput. Sci. 2 4 (2005) 715-722
39. L. Yunhui, L. Siwei, L. Aijun, Y. Hanbin, A new model selection criterion based on information geometry, in: 2004 Seventh International Conference on Signal Processing Proceedings, ICSP, 2004, pp. 1561-1564.