ACPSO—SVR结合的非线性建模预测算法研究

提出一种基于自适应混沌粒子群优化和支持向量机结合的非线性预测建模算法（ACPSO—SVR），引入ACPSO启发式寻优机制对SVR模型的超参数进行自动选取，在超参数取值范围变化较大的情况下，效果明显优于网格式搜索算法。选取UCI机器学习数据库中的Forestfires标准数据集进行测试，实验结果表明该方法具有较高的精度和良好的泛化能力．对于解决多变量的回归预测问题是一种有效的方法。最后给出了混合算法在碳一多相催化领域的两种典型应用．在反应动力学模型未知的情况下建立催化剂组份模型和操作条件模型，以及基于混合算法的最优催化剂设计框架。

Application of adaptive chaos particle swarm optimization and support vector regression for modeling and forecasting of nonlinear system

An effective relevance prediction algorithm is presented for nonlinear system forecasting and modeling based on adaptive chaos particle swarm optimization and support vector regression,namely ACPSO-SVR method.A heuristic optimization method ACPSO was introduced to automatic selection of hyper-parameters in SVR.The forest fires standard data set of UCI machine learning database was selected to test.The experimental results showed that the new method has high relatively precision and good generalization ability with a wide range of parameter values,better than that of mesh searching algorithm.It could be used as an effective method to solve the problems of multivariate regression predication.Moreover,two main applications were introduced in C1 heterogeneous catalysts yield,obtaining the catalyst composition model and the kinetic model,and building the optimization framework of catalyst development in laboratory.