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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/262368 |
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| author | Jie-sheng Wang Shuang Han Na-na Shen |
| author_facet | Jie-sheng Wang Shuang Han Na-na Shen |
| author_sort | Jie-sheng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ef8036e29941486299602735109162dc |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ef8036e29941486299602735109162dc2025-02-03T06:44:38ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/262368262368Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information FusionJie-sheng Wang0Shuang Han1Na-na Shen2School of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, ChinaSchool of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, ChinaSchool of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, ChinaFor 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.http://dx.doi.org/10.1155/2014/262368 |
| spellingShingle | Jie-sheng Wang Shuang Han Na-na Shen Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion The Scientific World Journal |
| title | Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion |
| title_full | Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion |
| title_fullStr | Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion |
| title_full_unstemmed | Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion |
| title_short | Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion |
| title_sort | improved gso optimized esn soft sensor model of flotation process based on multisource heterogeneous information fusion |
| url | http://dx.doi.org/10.1155/2014/262368 |
| work_keys_str_mv | AT jieshengwang improvedgsooptimizedesnsoftsensormodelofflotationprocessbasedonmultisourceheterogeneousinformationfusion AT shuanghan improvedgsooptimizedesnsoftsensormodelofflotationprocessbasedonmultisourceheterogeneousinformationfusion AT nanashen improvedgsooptimizedesnsoftsensormodelofflotationprocessbasedonmultisourceheterogeneousinformationfusion |