Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy ...Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.展开更多
基金The authors wish to thank the editor and anonymous referees for their constructive comments and recommendations, which have significantly improved the presentation of this paper. This work is supported by National Nature Science Foundation of China （Grant Nos. 60674021, 61273155）.
文摘Penicillin fermentation is an important part of microbial fermentation. Due to the existence of error date in the independent variables and dependent variables of the penicillin fermentation sample data, the accuracy of the model of penicillin fermentation is affected. In this paper, an amended harmony search （AHS） algorithm is developed to adjust the hyper-parameters of least squares support vector machine （LS-SVM） in order to build penicillin fermentation process model with prediction accuracy. The AHS algorithm is investigated by unconstrained benchmark functions with different characteristics. Compared with other several optimization approaches, AHS demonstrates a better performance. Moreover, using the simulation data from the PenSim simulation platform to validate the effectiveness of the penicillin fermentation process modeling, experiment results show that the penicillin fermentation process modeling based on the tuned LS-SVM by AHS possesses robustness and generalization ability.
