| PSO-SVR算法在发酵过程控制中的应用 |
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| 引用本文： | 陈 树,徐保国,王海霞,吴晓鹏.PSO-SVR算法在发酵过程控制中的应用[J].计算机工程与应用,2007,43(19):214-216. |
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| 作者姓名： | 陈 树 徐保国 王海霞 吴晓鹏 |
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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： | 针对发酵过程中生物参数难以实时在线测量的问题，建立了用于生物参数状态预估的支持向量机软测量模型。考虑到该支持向量回归模型的复杂性和推广能力的好坏很大程度上取决于其3个参数（?着，C，?酌）能否取到最优值，采用粒子群算法实现对参数（?着，C，?酌）的同时寻优。在此基础上，以L-天冬酰胺酶II为对象，建立其基于PSO-SVR的发酵过程产物浓度状态预估模型。发酵罐控制结果表明：该模型具有很好的学习精度和泛化能力，可实现对L-天冬酰胺酶II产物浓度的实时在线预估。
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| 关 键 词： | 支持向量回归（SVR） 状态预估 粒子群优化（PSO）算法 L-天冬酰胺酶II |
| 文章编号： | 1002-8331（2007）19-0214-03 |
| 修稿时间： | 2006-12 |
Study of fermentation process based on PSO-SVR |
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| CHEN Shu,XU Bao-guo,WANG Hai-xia,WU Xiao-peng.Study of fermentation process based on PSO-SVR[J].Computer Engineering and Applications,2007,43(19):214-216. |
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| Authors: | CHEN Shu XU Bao-guo WANG Hai-xia WU Xiao-peng |
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| Affiliation: | School of Communication and Control Engineering，Southern Yangtze University，Wuxi，Jiangsu 214122，China |
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| Abstract: | In accordance with the hardship to get real-time and on-line biology parameters in fermentation process,a soft sensor model based on Support Vector Machines(SVM) is established for estimating the biology parameters.It is well known that the model complexity and generalization performance of this Support Vector Regression(SVR) model depend on a good setting of the three parameters(ε,C,γ).In this article,an algorithm called Particle Swarm Optimization(PSO) is applied to optimize the parameters(ε,C,γ) at the same time.Based on the proposed method,a PSO-SVR model is developed to estimate the products concentration of L-Asparaginase Ⅱ.The results from fermenter control show that the PSO-SVR state estimation model has good learning accuracy and generalization performance so as to acquire the real-time and on-line estimation value of L-Asparaginase Ⅱ products concentration. |
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| Keywords: | Support Vector Regression（SVR） state estimation Particle Swarm Optimization（PSO） algorithm L-Asparaginase II |
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