Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion.
For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and b...
| Publicado en: | Scientific World Journal pp. 262368 - 262369 |
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| Autores principales: | , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
2014
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=103831764&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103831764 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1537744X 1BX5 jtl: Scientific World Journal issn: 1537744X maglogo: N pubinfo: dt: 2014 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 103831764 103831764 NLM24982935 2012634763 10.1155/2014/262368 NLM24982935 PMC4005072 103831764 ppf: 262368 ppct: 1 formats: tig: atl: Improved GSO optimized ESN soft-sensor model of flotation process based on multisource heterogeneous information fusion. aug: au: Wang, Jie-Sheng Han, Shuang Shen, Na-Na affil: School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, China ; National Financial Security and System Equipment Engineering Research Center, University of Science & Technology Liaoning, Anshan 114044, China. sug: subj: Algorithms ab: For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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