Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm
For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the c...
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| Main Authors: | , , , |
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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/10.1155/2014/208094 |
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| author | Jie-sheng Wang Shuang Han Na-na Shen Shu-xia Li |
| author_facet | Jie-sheng Wang Shuang Han Na-na Shen Shu-xia Li |
| author_sort | Jie-sheng Wang |
| collection | DOAJ |
| description | For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. |
| format | Article |
| id | doaj-art-27d87c84f8db4df2aa60f689cd6fcfa0 |
| 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-27d87c84f8db4df2aa60f689cd6fcfa02025-02-03T01:33:28ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/208094208094Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching AlgorithmJie-sheng Wang0Shuang Han1Na-na Shen2Shu-xia Li3School 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, ChinaSchool of Electronic and Information Engineering, University of Science & Technology Liaoning, Anshan 114044, ChinaFor meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy.http://dx.doi.org/10.1155/2014/208094 |
| spellingShingle | Jie-sheng Wang Shuang Han Na-na Shen Shu-xia Li Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm The Scientific World Journal |
| title | Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm |
| title_full | Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm |
| title_fullStr | Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm |
| title_full_unstemmed | Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm |
| title_short | Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm |
| title_sort | features extraction of flotation froth images and bp neural network soft sensor model of concentrate grade optimized by shuffled cuckoo searching algorithm |
| url | http://dx.doi.org/10.1155/2014/208094 |
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