关键词: 支持向量回归（SVR）, 状态预估, 粒子群优化（PSO）算法, L-天冬酰胺酶II

Abstract: In accordance with the hardship to get real-time and on-line biology parameters in fermentation process，a soft sensor model based on Support Vector Machines（SVM） is established for estimating the biology parameters.It is well known that the model complexity and generalization performance of this Support Vector Regression（SVR） model depend on a good setting of the three parameters（?着，C，?酌）.In this article，an algorithm called Particle Swarm Optimization（PSO） is applied to optimize the parameters（?着，C，?酌） at the same time.Based on the proposed method，a PSO-SVR model is developed to estimate the products concentration of L-Asparaginase II.The results from fermenter control show that the PSO-SVR state estimation model has good learning accuracy and generalization performance so as to acquire the real-time and on-line estimation value of L-Asparaginase II products concentration.

Key words: Support Vector Regression（SVR）, state estimation, Particle Swarm Optimization（PSO） algorithm, L-Asparaginase II