Abstract: This paper describes a method of signal process applied
on an end effects of Hilbert-Huang transform (HHT) to provide an
improvement in the reality of spectrum. The method is based on
back-propagation network (BPN). To improve the effect, the end
extension of the original signal is obtained by back-propagation
network. A full waveform including origin and its extension is
decomposed by using empirical mode decomposition (EMD) to obtain
intrinsic mode functions (IMFs) of the waveform. Then, the Hilbert
transform (HT) is applied to the IMFs to obtain the Hilbert spectrum of
the waveform. As a result, the method is superiority of the processing
of end effect of HHT to obtain the real frequency spectrum of signals.
Abstract: Control chart pattern recognition is one of the most important tools to identify the process state in statistical process control. The abnormal process state could be classified by the recognition of unnatural patterns that arise from assignable causes. In this study, a wavelet based neural network approach is proposed for the recognition of control chart patterns that have various characteristics. The procedure of proposed control chart pattern recognizer comprises three stages. First, multi-resolution wavelet analysis is used to generate time-shape and time-frequency coefficients that have detail information about the patterns. Second, distance based features are extracted by a bi-directional Kohonen network to make reduced and robust information. Third, a back-propagation network classifier is trained by these features. The accuracy of the proposed method is shown by the performance evaluation with numerical results.