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Figure 4 Steps for wavelet features extraction.

This paper presents a variable step-size updating algorithm for wavelet neural network (WNN) in setting the enhanced PID controller parameters.

Step 2: Calculate the noise variance σ ^ n 2 and the marginal variance σ ^ 2 for wavelet coefficient y k by using (21), (22) and (20).

The lifting steps for the forward transform with the Haar wavelet have been formulated by [31].

The individual steps including wavelet analysis technique, feature extraction, feature selection, and classification will be described in following subsections.

SNR is set at 8 for wavelet based method and 4 for our proposed method.

Subsequently, Cho and Lai simplified Goodman's constructive steps for compactly supported orthonormal scaling functions and provided an inductive method for constructing compactly supported orthonormal wavelets [6].

We briefly summarize the two steps (for more details, see Schramm et al. [ 1]): Step 1 (preprocessing of pathway information): To run a PathWave analysis which involves the use of wavelet transforms (see below), compact 2D grid representations of pathways are required.

In the first step, stationary wavelet transform (SWT) is used for noise reduction of the electrocardiogram (ECG) signals.

Wavelets had base frequencies sampled from 2 to 50 Hz in 25 logarithmically spaced steps, where each wavelet was characterized by three cycles.

The steps of the wavelet de-noising algorithm are.