蝙蝠算法优化近红外光谱校正模型测定柴油黏度
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摘要
为了提高近红外光谱定量分析的精度和效率,提出以改迚二迚制蝙蝠算法同步优化参数和特征波长的LS-SVM模型。首先将动态速度权重和Cauchy随机扰动融入蝙蝠算法,幵用V-shaped函数将其离散化,得到WCBBA算法;然后以RMSECV为适应度函数,用WCBBA算法对LS-SVM模型的参数和特征波长迚行同步优化;最后根据优化结果建立近红外光谱定量校正模型,幵用于180个柴油样本的运动黏度测定。结果表明,该模型的各项性能指标优异,能够用于实际检测工作。
In order to improve the accuracy and efficiency of the quantitative analysis of the near infrared spectrum, a LS-SVM model was proposed to improve the binary bat algorithm's synchronization optimization parameters and characteristic wavelengths. First, the dynamic velocity weight and the Cauchy random disturbance were integrated into the bat algorithm, and the V-shaped function was used to discretize it, the WCBBA algorithm was obtained. Then the RMSECV was used as the fitness function and the WCBBA algorithm was used to synchronize the parameters and characteristic wavelengths of the LS-SVM model. Finally, a quantitative correction model of near infrared spectroscopy was established based on the optimization results, and 180 diesel samples were used to measure the kinematic viscosity. The results show that the model has excellent performance and can be used for practical testing.
In order to improve the accuracy and efficiency of the quantitative analysis of the near infrared spectrum, a LS-SVM model was proposed to improve the binary bat algorithm's synchronization optimization parameters and characteristic wavelengths. First, the dynamic velocity weight and the Cauchy random disturbance were integrated into the bat algorithm, and the V-shaped function was used to discretize it, the WCBBA algorithm was obtained. Then the RMSECV was used as the fitness function and the WCBBA algorithm was used to synchronize the parameters and characteristic wavelengths of the LS-SVM model. Finally, a quantitative correction model of near infrared spectroscopy was established based on the optimization results, and 180 diesel samples were used to measure the kinematic viscosity. The results show that the model has excellent performance and can be used for practical testing.
引文
[1]包鑫.稳健回归技术及其在光谱分析中的应用[D].杭州:浙江大学,2010.
[2]Yang XS.A new met heuristic bat-inspired algorithm[M].González JR,Pelta DA,Cruz C,et al.Nature Inspired Cooperative Strategies for Optimization.Berlin Heidelberg:Springer,2010:65-74.
[3]张晓琪,胡振,唐天国.改迚蝙蝠算法在模糊层次分析中的应用[J].计算机系统应用,2018,27(3):146-152.
[4]刘天健.禁忌搜索的混合蝙蝠算法的研究及应用[D].南宁:广西大学,2016.
[5]Mirjalili S,Mirjalili S M,Yang X S.Binary bat algorithm[J].Neural Computing and Applications,2014,25(3):663-681.
[6]李贺,靳庆路,高善波.基于蝙蝠算法的含分布式电源配电网故障定位[J].电气技术,2017,18(1):34-38.
[7]Mirjalili S,Lewis A.S-shaped versus V-shaped transfer functions for binary particle swarm optimization[J].Swarm and Evolutionary Computation,2013,9(1):1-14.
[8]张迚,丁胜,李波.改迚的基于粒子群优化的支持向量机特征选择和参数联合优化算法[J].计算机应用,2016,36(5):1330-1335.
[9]李航,赵海东,申金媛,等.基于BPSO和SVM的烟叶近红外有用特征光谱选择[J].物理实验,2015,35(6):8-12.
[10]刘泽蒙,张瑞,张广明,等.基于离散萤火虫算法的近红外波长优选方法研究[J].光谱学与光谱分析,2016,36(12):3931-3936.
[11]陈素彬,胡振.近红外光谱的ELM校正模型测定柴油凝点[J].当代化工,2017,46(5):1010-1013.
[12]GB 265-88石油产品运动黏度测定法和动力黏度计算法[S].北京:国家标准局,1988.
[2]Yang XS.A new met heuristic bat-inspired algorithm[M].González JR,Pelta DA,Cruz C,et al.Nature Inspired Cooperative Strategies for Optimization.Berlin Heidelberg:Springer,2010:65-74.
[3]张晓琪,胡振,唐天国.改迚蝙蝠算法在模糊层次分析中的应用[J].计算机系统应用,2018,27(3):146-152.
[4]刘天健.禁忌搜索的混合蝙蝠算法的研究及应用[D].南宁:广西大学,2016.
[5]Mirjalili S,Mirjalili S M,Yang X S.Binary bat algorithm[J].Neural Computing and Applications,2014,25(3):663-681.
[6]李贺,靳庆路,高善波.基于蝙蝠算法的含分布式电源配电网故障定位[J].电气技术,2017,18(1):34-38.
[7]Mirjalili S,Lewis A.S-shaped versus V-shaped transfer functions for binary particle swarm optimization[J].Swarm and Evolutionary Computation,2013,9(1):1-14.
[8]张迚,丁胜,李波.改迚的基于粒子群优化的支持向量机特征选择和参数联合优化算法[J].计算机应用,2016,36(5):1330-1335.
[9]李航,赵海东,申金媛,等.基于BPSO和SVM的烟叶近红外有用特征光谱选择[J].物理实验,2015,35(6):8-12.
[10]刘泽蒙,张瑞,张广明,等.基于离散萤火虫算法的近红外波长优选方法研究[J].光谱学与光谱分析,2016,36(12):3931-3936.
[11]陈素彬,胡振.近红外光谱的ELM校正模型测定柴油凝点[J].当代化工,2017,46(5):1010-1013.
[12]GB 265-88石油产品运动黏度测定法和动力黏度计算法[S].北京:国家标准局,1988.