基于量化核最小均方算法的连续搅拌反应釜模型辨识

摘要：

连续搅拌反应釜(Continuous stirred tank reactor, CSTR)是典型的化工过程之一， 本文针对其强非线性特性， 提出了一种量化核最小均方(Quantized kernel least mean square, QKLMS)算法。 该算法基于一种简单在线矢量量化技术替代稀疏化准则， 可以对输入空间进行压缩， 从而抑制核学习算法中径向基函数(Radial basis function, RBF)结构的增长。 为验证该算法的有效性， 将其应用于CSTR过程的模型辨识中， 构建冷却剂流量与生成物浓度之间的非线性映射关系。 此外， 将所提算法与最小二乘支持向量机(Least square support vector machine, LS-SVM)、 回声状态网络(Echo state network, ESN)以及核极限学习机(Extreme learning machine with kernels, KELM)等算法进行比较。 实验结果表明， 在同等条件下， 本文所提算法具有更高的辨识精度和更好的在线学习能力。

The continuous stirred tank reactor (CSTR) is one of the typical chemical processes. Aiming at its strong nonlinear characteristics, a quantized kernel least mean square(QKLMS) algorithm is proposed. The QKLMS algorithm is based on a simple online vector quantization technology instead of sparsification, which can compress the input or feature space and suppress the growth of the radial basis function (RBF) structure in the kernel learning algorithm. To verify the effectiveness of the algorithm, it is applied to the model identification of CSTR process to construct a nonlinear mapping relationship between coolant flow rate and product concentration. In additiion, the proposed algorithm is further compared with least squares support vector machine (LS-SVM), echo state network (ESN), extreme learning machine with kernels (KELM), etc. The experimental results show that the proposed algorithm has higher identification accuracy and better online learning ability under the same conditions.