TY - GEN
T1 - Improvement of wavelet neural network hybrid systems performance by optimisation of two types of learning algorithms
AU - Tkacz, E.
AU - Kostka, P.
AU - Komorowski, D.
AU - Domider, T.
AU - Wrześniowski, A.
N1 - Publisher Copyright:
© 2002 Nanyang Technological University.
PY - 2002
Y1 - 2002
N2 - Two algorithms of hybrid, wavelet neural network system (WNS) structures, designed for specific application are presented. They describe the procedure of optimizing the wavelet transform (WT) scale parameter a in initial WNS layer. The type of learning algorithms depends on the features of wavelet basic function (WBF) used in WT signal decomposition. First algorithm, designed for Morlet WBF, which is expressed by analytical, differentiable formula, is based on modified error back propagation, used for multi-layer perceptron learning. Parameters of WNS with two remaining, tested WBFs - Db4 and Bior 2.4 were optimized according to the second proposed algorithm, which applies a predefined dyadic grid of scale a, and entropy analysis of Mallat multilevel decomposition, to choose the optimal decomposition level. The learning performance, characterized by speed and convergence of learning process, was verified in the field, specific for neural network applications. The base of non-stationary heart rate variability (HRV) signals, obtained from patients with coronary arm diseases of different level, divided into learning and verifying set was used. This type of signals was chosen, because the application of preliminary time-frequency analysis methods, especially the wavelet transform described in this paper, seems to be very good way for extraction the most important features, crucial for a given classification problem, included in HRV signal.
AB - Two algorithms of hybrid, wavelet neural network system (WNS) structures, designed for specific application are presented. They describe the procedure of optimizing the wavelet transform (WT) scale parameter a in initial WNS layer. The type of learning algorithms depends on the features of wavelet basic function (WBF) used in WT signal decomposition. First algorithm, designed for Morlet WBF, which is expressed by analytical, differentiable formula, is based on modified error back propagation, used for multi-layer perceptron learning. Parameters of WNS with two remaining, tested WBFs - Db4 and Bior 2.4 were optimized according to the second proposed algorithm, which applies a predefined dyadic grid of scale a, and entropy analysis of Mallat multilevel decomposition, to choose the optimal decomposition level. The learning performance, characterized by speed and convergence of learning process, was verified in the field, specific for neural network applications. The base of non-stationary heart rate variability (HRV) signals, obtained from patients with coronary arm diseases of different level, divided into learning and verifying set was used. This type of signals was chosen, because the application of preliminary time-frequency analysis methods, especially the wavelet transform described in this paper, seems to be very good way for extraction the most important features, crucial for a given classification problem, included in HRV signal.
KW - classification
KW - heart rate variability signal
KW - supervised learning
KW - wavelet-neural systems
UR - https://www.scopus.com/pages/publications/84964514030
U2 - 10.1109/ICONIP.2002.1198118
DO - 10.1109/ICONIP.2002.1198118
M3 - Conference contribution
AN - SCOPUS:84964514030
T3 - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing: Computational Intelligence for the E-Age
SP - 556
EP - 561
BT - ICONIP 2002 - Proceedings of the 9th International Conference on Neural Information Processing
A2 - Wang, Lipo
A2 - Fukushima, Kunihiko
A2 - Rajapakse, Jagath C.
A2 - Lee, Soo-Young
A2 - Yao, Xin
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Neural Information Processing, ICONIP 2002
Y2 - 18 November 2002 through 22 November 2002
ER -