Modeling Of Fermentation Process Based On Multiple Kernels Least Squares Support Vector Machine

Using fermentation model for the microorganism fermentation process and production process has been a hot topic for researchers in this area. The fermentation process model can find the suitable fermentation process for the protein drugs very quickly, and can monitor the production process. So it can promote the research and industrial production of protein drugs. The purpose of this paper is to establish an excellent and effective fermentation model with high accuracy of predictive capability for the protein fermentation process.It is very difficult to establish mechanism model. So we adopt the black-box modeling method. Least squares support vector machine (LS-SVM) method improves the neural network method that has easily into the local minimum points and poor ability to the training data out of the sample, but also improves the support vector machine (SVM) method that principle is too complex and takes a long time to modeling. The least square support vector machine modeling method has fast learning speed, good tracking performance and good regression accuracy on small sample model. So this paper uses the LS-SVM modeling method that suits small sample. We improve the sparsity and robustness of LS-SVM. In LS-SVM’s theory, choosing and constructing a suitable kernel is very important. So this paper firstly researches on the kernel and lists several single kernels. We evaluate each kernel from the mechanism point and the effect of practical application. The kernel is divided into global kernel and local kernel. The global kernel has generalization performance but weak learning ability. The local kernel has weak generalization performance but strong learning ability. This paper presents a multiple kernels function was linearly combined by global kernel and local kernel, and using it in the LS-SVM model. Multiple kernels method overcomes the shortcomings of the single kernel method. Then we optimize the mixed weight coefficient and the hyper parameter by using QPSO. The influence of the different kinds of kernel can be tuned by weight coefficient. Thus the model can get a better result on the regression accuracy and the generalization performance.In this paper, we will use the multiple kernels method to establish the LS-SVM fermentation model. This method applied in the microorganism fermentation modeling. We use QPSO to optimize the parameters. We can see that the LS-SVM model with multiple kernels has strong learning ability and generalization performance than the LS-SVM model with single kernel. The application results show that:the LS-SVM protein fermentation process model with multiple kernels has high accuracy and satisfactory prediction effect.