基于热扩散核密度确定密度峰值法的历史工况识别

毕荣山,韩智慧,陶少辉,孙晓岩,项曙光

Recognizing historical operating conditions by determining the density peaks at kernel density estimation of heat diffusion

Rongshan BI,Zhihui HAN,Shaohui TAO,Xiaoyan SUN,Shuguang XIANG

表1 仿真多模态过程的工况识别结果

Table 1 Recognition results of simulation multiple operating modes

项目	工况个数	每种工况的先验概率	每种工况下变量x₁, x₂, x₃的平均值	相对偏差
实际值	3	0.25	11.777,296.288,12.410	—
		0.25	20.467,192.899,354.307	—
		0.5	10.351,19.900,152.718	—
本文方法	3	0.25	11.777,296.288,12.410	0,0,0
		0.25	20.467,192.899,354.307	0,0,0
		0.5	10.351,19.900,152.718	0,0,0
K-均值法（K = 3）	3	0.25	11.777,296.288,12.410	0,0,0
		0.25	20.467,192.899,354.307	0,0,0
		0.5	10.351,19.900,152.718	0,0,0
K-均值法（K = 4）	4	0.25	11.777,296.288,12.410	0,0,0
		0.136	21.4211,209.428,388.063	4.66,8.57,9.53
		0.114	19.3326,173.233,314.144	5.54,10.2,11.34
		0.5	10.3508,19.900,152.718	0,0,0
GMM(F-J)法（K = 4）	4	0.25	11.777,296.288,12.410	0,0,0
		0.203	10.0372,15.8427,152.52	-2.84,-19.7,0.01
		0.25	20.467,192.899,354.307	0,0,0
		0.297	10.5662,22.6857,152.853	1.88,13.03,-0.05
GMM(F-J)法（K = 5）	4	0.25	11.777,296.288,12.410	0,0,0
		0.203	10.0372,15.8426,152.52	-3.03,-20.39,-0.13
		0.25	20.467,192.899,354.307	0,0,0
		0.297	10.5662,22.6857,152.853	2.08,14,0.09