Abstract
In order to solve the problem when monitoring and controlling penicillin fermentation processes, So an intelligent modeling method based on Quantum Particle Swarm Optimization (QPSO) algorithm and Weighted Least Squares Support Vector Machines (WLS-SVM) is presented, which can overcome the noise of sample data, the high non-linear. Applied the method in penicillin fermentation processes and compared with the Pensim simulation platform data, it obviously found that the WLS-SVM is superior to the unweighted LS-SVM modeling method that has a better estimation accuracy and robustness.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (21206053,21276111); General Financial Grant from China Postdoctoral Science Foundation (2012M511678); A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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Xiong, W., Wang, X., Zhang, Q., Xu, B. (2013). Modeling for Penicillin Fermentation Process Based on Weighted LS-SVM and Pensim. In: Sun, Z., Deng, Z. (eds) Proceedings of 2013 Chinese Intelligent Automation Conference. Lecture Notes in Electrical Engineering, vol 255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38460-8_31
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DOI: https://doi.org/10.1007/978-3-642-38460-8_31
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