Model identification of continuous stirred tank reactor based on QKLMS algorithm

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.

基于模糊GO-FLOW法的动车组车载系统可靠性分析

车载系统作为列车控制系统的重要组成部分,其可靠性的高低水平直接关系到整个列车的运营安全。首先根据动车组车载系统的工作原理以及故障模式,建立CTCS-3列控车载系统的GO-FLOW模型,按照车载系统的工作流程选取7个时间点进行可靠性分析;其次结合L-R型模糊算子,并选取合适的参量,改进传统GO-FLOW法的定量计算模型;最后采用改进后的模糊GO-FLOW定量分析模型,计算各部件在7个时间点的输出信号强度,结果表明,模糊GO-FLOW模型更具有逻辑性,能够更精确的表达系统可靠性的不确定性。该分析结果为动车组列车运营的可靠性提供了数据分析基础。