| 基于APSO—LSSVM的软测量建模研究 |
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| 引用本文： | 吴洲,田鹏,潘丰.基于APSO—LSSVM的软测量建模研究[J].自动化技术与应用,2009,28(1):6-9. |
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| 作者姓名： | 吴洲 田鹏 潘丰 |
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| 作者单位： | 江南大学,通信与控制工程学院,江苏,无锡,214122 |
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| 基金项目： | 国家高技术研究发展计划(863计划) |
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| 摘 要： | 针对最小二乘支持向量机在生化过程建模中的重要建模参数值选择问题，提出利用具有较强的全局搜索能力的自适应粒子群（APSO）优化算法，对最小二乘支持向量机建模过程中的重要参数进行优化调整，每一个粒子的位置向量对应一组最小二乘支持向量机建模的参数。利用参数优化调整后得到的具有较优拟和预测效果的模型对谷氨酸发酵过程进行预测，仿真结果表明该方法能使模型取得较好的预测效果。
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| 关 键 词： | 最小二乘支持向量机 粒子群 参数选择 |
Modeling for Soft Sensor Based on the Adaptive Particle Swarm Optimization and Least Squares Support Vector Machine |
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| WU Zhou,TIAN Peng,PAN Feng.Modeling for Soft Sensor Based on the Adaptive Particle Swarm Optimization and Least Squares Support Vector Machine[J].Techniques of Automation and Applications,2009,28(1):6-9. |
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| Authors: | WU Zhou TIAN Peng PAN Feng |
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| Affiliation: | (School of Communication and Control Engineering, University, Wuxi 214122 China ) |
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| Abstract: | To overcome the difficulties of getting better parameter values in the biochemical modeling with Least Squares Support Vector Machine, an adaptive particle swarm optimization method is proposed where each particle indicates a group of the LSSVM parameters. The proposed method is applied to establish a soft-sensor model for the Glutanic acid fermentation process. The simulation result indicated that the method can obtain a better forecast effect in the glutanic acid fermentation process. |
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| Keywords: | LSSVM particle swarm optimization parameters selection |
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