Name
Title | ||
|---|---|---|
Kpca Based Multi-Spectral Segments Feature Extraction And Ga Based Combinatorial Optimization For Frequency Spectrum Data Modeling |
Abstract | ||
|---|---|---|
Mill load (ML) estimation plays a major role in improving the grinding production rate (GPR) and the product quality of the grinding process. The ML parameters, such as mineral to ball volume ratio (MBVR), pulp density (PD) and charge volume ratio (CVR), reflect the load inside the ball mill accurately. The relative amplitudes of the high-dimensional frequency spectrum of shell vibration signals contain the information about the ML parameters. In this paper, a kernel principal component analysis (KPCA) based multi-spectral segments feature extraction and genetic algorithm (GA) based Combinatorial optimization method is proposed to estimate the ML parameters. Spectral peak clustering algorithm based knowledge is first used to partition the spectrum into several segments with their physical meaning. Then, the spectral principal components (PCs) of different segments are extracted using KPCA. The candidate input features are serial combinated with mill power. At last, GA with Akaike's information criteria (AIC) is used to select the input features and the parameters for the least square-support vector machine (LS-SVM) simultaneously. Experimental results show that the proposed approach has higher accuracy and better predictive performance than the other normal approaches. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/CDC.2011.6161537 | 2011 50TH IEEE CONFERENCE ON DECISION AND CONTROL AND EUROPEAN CONTROL CONFERENCE (CDC-ECC) |
Keywords | Field | DocType |
support vector machines,vibrations,principal component analysis,sensors,combinatorial optimization,least squares support vector machine,principal component,data models,kernel,spectrum,frequency spectrum,genetic algorithm,kernel principal component analysis,support vector machine,genetic algorithms,feature extraction,data model,optimization,acoustics,ball milling | Kernel (linear algebra),Mathematical optimization,Akaike information criterion,Pattern recognition,Computer science,Support vector machine,Combinatorial optimization,Kernel principal component analysis,Feature extraction,Artificial intelligence,Cluster analysis,Principal component analysis | Conference |
Volume | Issue | ISSN |
null | null | 0743-1546 |
Citations | PageRank | References |
1 | 0.40 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Tang | 1 | 526 | 148.30 |
| Tianyou Chai | 2 | 2014 | 175.55 |
| Wen Yu | 3 | 1 | 0.40 |
| Lijie Zhao | 4 | 41 | 9.72 |
| S. Joe Qin | 5 | 424 | 36.37 |