Measuring Wavelet Suitability with Symmetric Distance Coefficient

Author : Pah, Nemuel Daniel;

Wavelet network introduced by Zhang and Benveniste in 1992, is a network that combines wavelet transform and artificial neural network to perform wavelet transform in parallel and adaptive approach with learning capability. In its learning process, wavelet network iteratively adjust wavelet transform parameters to accurately transform significant components in input signal to wavelet space. Wavelet function is the most difficult parameter to be adjusted, since there is no analytic parameter that links various type of wavelet function. This paper reports the experiments conducted to investigate the possibility of using symmetric distance as a parameter to measure wavelet suitability, analytically. The results suggested that there was a strong correlation between symmetric distance coefficients and qualitative inspection of wavelet suitability. The result also suggested that the technique could also be used in high density signal.

Keyword : Wavelet Transform, Wavelet Network, Dymmetricd

Sumber :

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