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Recently, Deep belief networks(DBNs) have been applied in classification and regression, proved to be superior to general algorithms. But its powerful deep feature extraction ability has not yet been fully played so that a novel algorithm, multi-scale DBNs fusing wavelet transform(WT), is proposed in this paper. Based on the advantages of predicting high frequency components from WT by DBN confirmed,...
A diagnosis method basing on neural network classifier, genetic algorithm (GA) and wavelet transform is proposed for a pulse width modulation voltage source inverter. It is used to detect and identify the transistor open-circuit fault. BP neural network (BPNN) is capable of recognition. However, it has shortcomings obviously. These are just advantages of GA, which has ability of global search. Thus...
This paper focuses on traffic flow forecasting approach based on soft computing tools. The soft computing tools used is Particle Swarm Optimization (PSO) with Wavelet Network Model(WNM). The forecast of short-term traffic flow in timely and accurate is one of important contents of intelligent transportation system research. The modelling of traffic characteristics and the prediction of future traffic...
In this paper, surface electromyographic signal is analyzed by wavelet transform. The feature vectors are built by extracting the singular value of the wavelet coefficients. The multi-class support vector machine classifier is designed by using four kinds of multi-class classification approaches, and completed the eight class surface EMG pattern classification. The SVM classifier is applied to the...
This paper suggests a novel method named DOSCWTGRNN, which is based on generalized regression neural network (GRNN) with direct orthogonal signal correction (DOSC) and wavelet transform (WT) as a preprocessing tool for the simultaneous spectrophotometric determination of o-nitro-aniline, m-nitro-aniline and p-nitro- aniline. DOSC was applied to remove structured noise that is unrelated to the concentration...
This paper suggests a novel method named WTGRNN, which is based on generalized regression neural network (GRNN) with wavelet transform (WT) as a pre-processing tool for the simultaneous spectrophotometric determination of o-nitro-aniline, m-nitro-aniline and p-nitro-aniline. Wavelet representations of signals provide a local time-frequency description, thus in the wavelet domain, the quality of noise...
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