Online Modeling Method Based on Dynamic Time Warping and Least Squares Support Vector Machine for Fermentation Process
A new online local modeling method is proposed for fed-batch fermentation processes based on dynamic time warping (DTW) and least squares support vector machine (LS_SVM). In this method, a set of data within the sliding window is set as a query sequence in the current process, and then search for the most similar sub-sequence from the historical batch database to form the training set. At last, this training set will be used for build online local model based on LS_SVM. A forecast model of penicillins concentration is constructed based on the proposed method and off-line global modeling method using the data generated by the Pensim fermentation simulation platform. The simulation result shows that this method has a higher forecast accuracy and dynamic adaptability compared with the traditional offline modeling method.
Gong Yanjie Gao Xuejin Wang Pu Qi Yongsheng
College of Electronic Information and Control Engineering Beijing University of Technology Beijing 1 College of Information Engineering Inner Mongolia University of Technology Huhhot Inner Mongolia 010
国际会议
The 8th World Congress on Intelligent Control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)
济南
英文
481-485
2010-07-07（万方平台首次上网日期，不代表论文的发表时间）