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 ...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.展开更多
基金National Natural Science Foundation of China(No.51467008)Scientific Research Projects of Colleges and Universities in Gansu Province(Nos.2018C-10,2017D-09)。
文摘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.
