Decoupling Control Based on Support Vector Machine Inverse System in a L-lysine Fermentation Process

2008 ◽  
Author(s):  
Yonghong Huang ◽  
Yukun Sun ◽  
Bo Wang ◽  
Xiaofu Ji
Keyword(s):  
Support Vector Machine ◽  
Fermentation Process ◽  
Inverse System ◽  
Decoupling Control ◽  
Support Vector ◽  
Lysine Fermentation

Decoupling Control of Bearingless Induction Motor Based on LS-SVM Inverse System

2014 ◽  
Vol 703 ◽  
pp. 331-334 ◽  
Author(s):  
Lu Ting Miao ◽  
Yu Xin Sun ◽  
Huang Qiu Zhu ◽  
Xian Xing Liu
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Induction Motor ◽  
Inverse System ◽  
Decoupling Control ◽  
Support Vector ◽  
Electromagnetic Torque ◽  
Fuzzy Pid Control ◽  
Machine Method ◽  
Static State

According to the strong coupling between the electromagnetic torque and radial suspension force, and the difficulty of establishing a precise mathematical model of bearingless induction motor, least squares support vector machine is proposed in this paper. The inverse model approximated by the LS-SVM is connected with the original system, decoupling a complex nonlinear multivariable system into four relatively independent pseudo-linear sub-systems of single input and single output. At the same time, fuzzy PID control strategy is introduced to the pseudo linear system to ensure the robustness and anti-jamming ability of the control system. Then the performance is simulated with Matlab/ Simulink. The theory research and simulation experiment have validated that the decoupling control of inverse system based on least squares support vector machine method can be successfully realized, which between the electromagnetic torque and radial suspension force, and the good performance of dynamic and static state of system can be also obtained.

Research on SVM Cell Concentration Prediction of Erythromycin Fermentation Process Based on Ant Colony Optimization

2013 ◽  
Vol 734-737 ◽  
pp. 2998-3002
Author(s):  
Li Tian ◽  
Qiang Qiang Wang ◽  
An Zhao Cao
Keyword(s):  
Support Vector Machine ◽  
Fermentation Process ◽  
Ant Colony Algorithm ◽  
Cell Concentration ◽  
Ant Colony ◽  
Support Vector ◽  
Nonlinear Complexity ◽  
Svm Model ◽  
Concentration Prediction

Due to disadvantages of nonlinear, complexity and uncertainty of fermentation process, a research on cell concentration prediction of erythromycin fermentation process was carried out. Combining the optimization ability of ant colony algorithm and the regression ability of support vector machine, an ACO-SVM model is built. Case study shows that, the model is more accurate and more effective for the cell concentration prediction than ANN and SVM model.

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