基于混沌最小二乘支持向量机的软测量建模

Soft Sensor Modeling Based on Chaos Optimization Algorithm and Least Squares Support Vector Machines

提出一种改进算法,用来解决现有最小二乘支持向量机方法在处理大规模样本软测量建模问题时出现的模型结构复杂、失去支持向量稀疏性且正规化参数和核参数难以确定等问题。对样本集进行预处理,通过计算样本间欧氏距离进行样本相似程度分析,去除样本集中1/3的样本以简化支持向量机模型结构并提高计算速度。定义了一种混沌映射构成混沌系统并分析了其遍历性。应用改进的混沌优化算法优化最小二乘支持向量机模型参数以提高模型的拟合精度和泛化能力。将改进算法用于丙烯腈收率软测量建模中,仿真实验结果表明:模型精度较高,泛化性能好,满足现场测量要求。

When dealing with soft-sensor modeling problem with lots of samples,the traditional least squares support vector machines（LS-SVM） algorithm has some shortcomings,such as,the complexity of model structure,the loss of sparseness,and the difficulty in selecting normalizing parameter and kernel parameter.In this paper,an improved algorithm is proposed to overcome the above drawbacks.The samples are firstly pre-processed and the similarities between samples are analyzed by computing their Euclidian distances.Moreover,one third of the original sample points is removed so that the SVM model structure is simplified and the computing speed is increased.A chaotic map is defined and the ergodicity of chaos is analyzed.Then,the proposed chaos optimization algorithm is used to optimize the LS-SVM model parameters so as to raise the fitting accuracy and enhance its generalization ability.Finally,the proposed method is applied to the soft sensor modeling of the acrylonitrile yield.It is shown from simulation results that this model has higher prediction precision and better generalization ability,and can satisfy the requirement of spot measurement.