基于自适应变异PSO-SVM的APU性能参数预测模型

doi:10.12305/j.issn.1001-506X.2021.02.27

Abstract

Abstract:

In order to improve the accuracy of performance parameter prediction of auxiliary power unit (APU), and solve the problem of parameter selection of support vector machine (SVM) model in practical use, adaptive mutation particle swarm optimization (PSO) is adopted to optimize the selection of penalty parameters and kernel parameters of SVM. And a prediction model of APU performance parameters based on adaptive mutation PSO algorithm is proposed. Furthermore, the influence of different prediction steps on short-term prediction accuracy is analyzed. The performance parameters of an APU are verified and compared with other prediction models. The experimental results show that for the prediction of exhaust gas temperature (EGT), the mean absolute percentage error (MAPE) of adaptive mutation PSO-SVM model is 47% lower than that of standard PSO-SVM model; for the prediction of oil temperature (OT), the MAPE of adaptive mutation PSO-SVM model is 29% lower than that of standard PSO-SVM, which provides some reference for short-term prediction of APU performance trend.

Key words: auxiliary power unit (APU), particle swarm optimization (PSO) algorithm, performance parameter prediction, support vector machine (SVM)

CLC Number:

V241.07

Kun WANG, Shuxian HOU, Li WANG. APU performance parameter prediction model based on adaptive variation PSO-SVM[J]. Systems Engineering and Electronics, 2021, 43(2): 526-536.

Figures/Tables 12

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控制变量			EGT	OT	RS	BAF	BAP	GL
气压海拔外界温度	GL	相关性	0.619	0.579	0.395	0.260	-0.025	1.000
		显著性(双侧)	0	0	0	0	0
		DF	638	638	638	638	638	0
	BAF	相关性	0.709	0.389	0.279	1.000	-0.056	0.260
		显著性(双侧)	0	0	0		0	0
		DF	638	638	638	0	638	638

模型	MAE	MSE	MAPE/%	RMSE
小波神经网络	9.571	0.072	3.98	0.268
Elman神经网络	6.434	0.036	2.81	0.189
标准PSO-SVM	2.826	0.011	1.19	0.109
SVM	4.399	0.041	1.72	0.202
LSSVM	3.128	0.032	1.35	0.178
自适应变异PSO-SVM	1.603	0.009	0.63	0.095
BP神经网络	7.977	0.052	3.47	0.228

模型	MAE	MSE	MAPE/%	RMSE
小波神经网络	0.929	0.253	1.06	0.503
Elman神经网络	0.164	0.088	0.20	0.296
标准PSO-SVM	0.213	0.061	0.24	0.246
SVM	0.310	0.087	0.35	0.294
LSSVM	0.235	0.074	0.25	0.272
自适应变异PSO-SVM	0.152	0.045	0.17	0.212
BP神经网络	0.192	0.062	0.22	0.248

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APU performance parameter prediction model based on adaptive variation PSO-SVM

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