Improvement of wavelet neural network hybrid systems performance by optimisation of two types of learning algorithms

ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age

Volumn 2, Issue , 2002, Pages 556-561

  Tkacz, E ^a,b,c   Kostka, P ^a,c   Komorowski, D ^a,c   Domider, T ^a   Wrzesniowski A ^a  

^a SILESIAN UNIVERSITY OF TECHNOLOGY   (Poland)

^b INSTITUTE OF MEDICAL TECHNOLOGY AND EQUIPMENT   (Poland)

^c Fundacja Rozwoju Kardiochirurgii Zabrze   (Poland)

Author keywords

classification; heart rate variability signal; supervised learning; wavelet neural systems

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; CLASSIFICATION (OF INFORMATION); HEART; HYBRID SYSTEMS; INFORMATION SCIENCE; LEARNING SYSTEMS; OPTIMIZATION; SIGNAL PROCESSING; SUPERVISED LEARNING; WAVELET ANALYSIS; WAVELET DECOMPOSITION; WAVELET TRANSFORMS;

HEART RATE VARIABILITY; HEART RATE VARIABILITY SIGNALS; MULTI LAYER PERCEPTRON; MULTILEVEL DECOMPOSITION; NEURAL NETWORK APPLICATION; TIME-FREQUENCY ANALYSIS METHODS; WAVELET NEURAL NETWORKS; WAVELET NEURAL SYSTEMS;

LEARNING ALGORITHMS;

EID: 84964514030     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICONIP.2002.1198118     Document Type: Conference Paper

References (14)

1. Poggio T., Girosi F.:Networks for approximation and learning, Proc. IEEE, 78(9), Sept 1990.
2. Hornik K., Multilayer feedforward networks are universal approximators, Neural Networks, vol.2, 1989.
3. Broomhead D.S., Lowe D., Multivariete functional interpolation and adaptive networks, Complex Systems, 2:321-355, 1988.
4. Hartman E.J., Keeler J.D., Kowalski J.M., Layered neural networks with Gaussian hidden units as universal approximations, Neural Computation, 2:210:215, 1990.
5. Vetterli M., Wavelets and filter banks, IEEE Transaction on Signal Processing, 9(40):2207-2232, Sep 1992.
6. Lawton W.M., Tight frames of compactly supported wavelets, Journal of Mathematical Physics, 31(8):1898-1901, Aug. 1990.
7. Mallat S., A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transaction on Pattern Analysis, 7(11):674-693, 1989.
8. Zhang Q., Benveniste A., Wavelet networks, IEEE Transaction on Neural Network 3(6):889-898, Sep 1992.
9. Pati Y.C., Krishnaprasad P.S., Analysis and synthesis of feed-forward neural networks using discrete affine wavelet transformations, IEEE Trans. on Neural Networks, 4:73-85, Jan. 1993.
10. Bakshi, Bhavik, Stephanopulos, Wave-Net: a multiresolution, hierarchical neural network with localized learning, AIChE J. 39:57-81, 1993
11. Chen, Junghui, Bruns, WaveARX neural network development for system identification using a systematic design synthesis, Ind. Eng. Chem. Res. 34:4420-4435, 1995.
12. Kostka P., Tkacz E.J., Nawrat Z., Malota Z., An Application of Wavelet Neural Networks (WNN) for Heart Valve Prostheses Characteristics, World Congress on Medical Physics and Biomedical Engineering - Chicago 2000.
13. Tkacz E.J., Kostka P., An Application of Wavelet Neural Network for Classification Patients with Coronary Artery Disease Based on HRV Analysis, World Congress on Medical Physics and Biomedical Engineering - Chicago 2000.
14. Tadeusiewicz R., Sieci neuronowe, Akademicka Oficyna Wydawnicza, 1993.