It increases the precision and convergent rate of the model,and is especially suitable for local signal analysis, signal-noise separation and detection of jumping signals with the characteristics of multi-scale interpolation and sparse variation, thus enhances the generalization ability of the SVM, recognition efficiency and computation burden is alleviated.
On the base of the signal-noise separation condition of the seismic reflection in near surface engineering geophysics, we study the multi-radial mid-value filtering method which applies for processing pre-stack trace gather.
Based on the wavelet decomposition and conditions of the support vector kernel function, a novel multi-dimension admissible support vector wavelet kernel function is presented, which is not only approximately orthonormal, but also is especially suitable for local signal analysis, signal-noise separation and detection of jumping signals, thus enhances the generalization ability of the support vector machine (SVM).
To sum up, the multi-radial mid-value filtering can separate the linear noise any direction effectively, and has the characters such as the high signal-noise separation, highly keeping amplitude and high resolution.
The feature of fault signal, which is obtained by singular signal inspection, signal noise separation and signal frequency range analysis by means of wavelet analysis, can be used to identify the position and intensity of the fault.
It compares the result processed by BP network with the result processed by classical spectrum estimation, and indicates the good characterisitic of BP network in separation of signal and noise.
The wavelet analysis is one brand-new signal processing method, which decomposes each kind of different frequency component to the frequency band that mutually not overlap. This method provides an effective way for the signal filter, the separation of signal and noise, and the characteristic withdrew.
The emulation calculation of separation of signal and noise is carried out, and the possible application of wavelet analysis in several areas of on line monitoring is discussed.
In accordance with separation of signal and noise for high-frequency square wave signals,the technical strategy of intelligent pulse width digital filtering is proposed,a practical pulse width digital filtering circuit is designed to eliminate the peak interference in measuring signals.