Least square method support vector machine-based generalized prediction method in lysozyme fermentation process

Abstract

The invention discloses a least square method support vector machine-based generalized prediction method in a lysozyme fermentation process. The prediction method comprises the following steps of establishing a non-linear prediction model, and training a least square method support vector machine by using production data with higher yield screened from tank fermentation; performing real-time linearization on the input and output non-linear prediction model, setting a reference trajectory, rolling-optimizing controller design, and intelligently embedding an LS-SVM (least square-support vector machine)-based generalized prediction control algorithm in the lysozyme fermentation process into an upper computer. According to the method, the least square method support vector machine and the generalized prediction control are combined, so the QP problem of time consumption of solving in the solving process with the model is avoided, the operation is simple, the convergence speed is speed, and the precision is high. A genetic algorithm and the rolling optimizing in the generalized prediction control are combined, so the robustness of a system is enhanced, and the lag and disturbance of the system are effectively overcome.

Description

technical field [0001] The invention relates to the advanced control field of microbial fermentation process, in particular to a generalized predictive control method for feed feeding in lysozyme fermentation process based on least squares support vector machine (LS-SVM). technical background [0002] The highly nonlinear, time-varying and uncertain multi-variable coupling system in the microbial fermentation process is very complicated because it involves the growth and reproduction of living organisms. The modeling of the fermentation process is a basic problem in fermentation engineering, it serves for the control and optimization of the fermentation process, and accurate modeling is conducive to obtaining better control strategies and optimization methods. Support Vector Machine (SVM for short) is a better way to realize the idea of ​​structural risk minimization, and it is the youngest part of statistical learning theory. The support vector machine takes the training e...

AI Technical Summary

Problems solved by technology

The support vector machine takes the training error as the constraint condition of the optimization problem, takes the minimization of the confidence range value as the optimization goal, maps the input vector to a high-dimensional feature space through a certain nonlinear mapping, and solves a linear constraint quadratic The planning problem obtains the global optimal solution and the number of constraints is equal to the sample size, so when the sample size is large, the training takes a long time

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