基于改进的遗传算法优化支持向量机的精馏塔故障诊断

针对支持向量机（SVM）参数的选取困难，提出了利用改进的遗传算法（IGA）对其参数进行优化。IGA采用代沟选择和可变交叉概率，确保当前种群中最适应的个体总是被连续传播到下一代，并使进化后期优化的对象比较容易稳定，计算效率提高。将基于改进遗传算法优化的SVM（IGA-SVM）训练算法应用于某醋酸共沸精馏塔的故障诊断，仿真实验结果表明：对比标准GA-SVM算法，IGA-SVM算法对故障数据能够得到较优的分类辨识结果，且该算法训练速度更快，便于工程应用，对精馏塔的故障诊断有显著的指导作用。

Fault Diagnosis of Distillation Column Based on Improved Genetic Algorithm Optimization-Based Support Vector Machine

Aiming at the shortcoming that the parameters of support vector machine (SVM) are not easily chosen, this paper utilizes the improved genetic algorithm (IGA) to optimize the parameters of SVM. By using generation gap selection and alterable crossover rate, the IGA algorithm can ensure that the present individuals of best adaptiveness are continuously passed to the next generation. Moreover, the optimized object is easily stabilized during the later stage of evolution, and the calculating efficiency can be raised. Finally, the proposed algorithm is applied to the fault diagnosis of acetic acid azeotropic distillation column. The simulation results show that this algorithm can obtain better classification result and faster training rate than the standard GA-SVM algorithm.