面向阶段任务的携行器材品种确定方法

doi:10.12305/j.issn.1001-506X.2024.06.23

Abstract

Abstract:

Maintenance material is the basis for effective implementation of maintenance support. It is the key to make decision of carrying material variety. In order to solve the problem which is difficult to determine the carrying material varieties due to various types of material in the stage task, the complex influencing factors and unclear association relationchip, a method for determining carrying material varieties of stage task combined with the improved sparse kernel principal component analysis (SKPCA) and long short-term memory (LSTM) neural network model is proposed. On the basis of analyzing the influencing factors and characteristic indicators of stage task carrying material, an improved SKPCA dimension reduction method based on elastic penalty is proposed which can reduce the material features dimensionality and obtain low-dimensional sparse feature vectors to enhance the data interpretability. The chaotic sequence is used to improve the flower pollination algorithm (FPA) which optimizing the LSTM hyperparameters and the chaotic FPA-LSTM prediction model is constructed. Through the example analysis by the exercise, the scientificity and feasibility of the proposed method are verified.

Key words: carrying material, stage task, sparse kernel principal component analysis (SKPCA), influencing factor analysis, flower pollination algorithm (FPA), long short-term memory (LSTM) neural network

CLC Number:

Weiyi WU, Yunxian JIA, Xiangzheng JIANG, Xianming SHI, Jie LIU, Bin LIU, Enzhi DONG, Xi ZHU. Method for determining for carrying material varieties of stage task[J]. Systems Engineering and Electronics, 2024, 46(6): 2054-2064.

Figures/Tables 17

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任务	阶段时序	阶段任务	部件类型
综合行动	1	战斗准备	部件1部件2部件3
综合行动	2	机动	部件1部件2部件3
火力打击	3	火力压制	部件1部件2部件3部件4部件5
火力打击	4	火力支援	-
侦察	5	武装侦察	-
侦察	6	-	-
$\vdots$	7	$\vdots$	$\vdots$

器材品种	可靠性/h	关键性	维修复杂性	可获取性	单价/元	使用时间/h	供应周转时间/h	体积/m³	质量/kg	单装用数	维修时间/min	使用环境要求	携行结果
器材1	91	1	1	0	500	50	4	2.4	6.3	5	25	1	1
器材2	144	1	1	0	450	120	3	1.5	3.6	4	20	1	1
器材3	888	2	1	0	900	250	2	3.23	2.2	1	45	3	0
器材4	274	3	1	1	800	75	1	1.21	1.33	3	40	4	0
器材5	750	1	1	1	727	100	2	2.97	0.91	1	45	2	0
$\vdots$

向量	PCA			KPCA			改进SKPCA
向量	PCA1	PCA2	PCA3	PCA1	PCA2	PCA3	PCA1	PCA2	PCA3
X1	-0.40	0.22	-0.21	-0.39	0.48	-0.07	-0.12	0.00	-0.04
X2	-0.41	0.19	-0.24	-0.39	0.47	0.13	-0.04	-0.07	0.00
X3	0.12	0.54	0.14	0.02	0.35	0.30	-0.03	0.00	-0.19
X4	-0.17	0.46	0.35	-0.02	-0.21	0.40	0.00	0.16	0.00
X5	-0.06	-0.17	0.48	0.12	-0.34	0.09	-0.11	0.09	0.00
X6	-0.28	-0.01	0.48	-0.38	0.08	0.34	-0.02	0.03	-0.15
X7	-0.40	-0.19	0.25	-0.47	-0.50	0.51	0.24	0.00	0.19
X8	-0.29	-0.19	-0.24	0.27	0.31	-0.33	-0.11	0.00	-0.41
X9	-0.36	0.02	-0.21	-0.37	0.30	-0.38	0.31	-0.15	0.00
X10	-0.38	-0.25	-0.12	-0.43	-0.52	-0.47	0.00	0.00	0.09
方差/%	32.40	18.30	14.40	40.80	19.20	15.30	52.76	23.78	10.72
累计方差/%	32.40	50.70	65.10	40.80	50.00	75.30	52.76	76.54	87.26

主成分	PCA1	PCA2	PCA3	PCA4	PCA5
X1	-0.12	0.00	-0.04	0.00	0.23
X2	-0.04	-0.07	0.00	-0.06	0.00
X3	-0.03	0.00	-0.19	0.04	0.00
X4	0.00	0.16	0.00	0.16	-0.35
X5	-0.11	0.09	0.00	0.00	0.18
X6	-0.02	0.03	-0.15	0.00	0.00
X7	0.24	0.00	0.19	0.37	0.00
X8	-0.11	0.00	-0.41	0.18	0.00
X9	0.31	-0.15	0.00	0.00	0.01
X10	0.00	0.00	0.09	0.15	-0.28
方差/%	52.76	23.78	10.72	6.86	0.90
累计方差/%	52.76	76.54	87.26	94.12	95.02

模型	降维维数	决策准确率	MAE	RMSE	算法时间/s
PCA-LSTM	9	0.78	0.136	0.382	30.27
KPCA-LSTM	6	0.81	0.131	0.374	27.61
改进SKPCA-LSTM	5	0.88	0.126	0.355	28.79

Method for determining for carrying material varieties of stage task

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测试集(器材品种)	器材1	器材2	…	器材7	…	器材50
预测结果	0	1	$\vdots$	0	$\vdots$	1
实际结果	0	1	$\vdots$	1	$\vdots$	0
MAE			0.14
RMSE			0.374
决策准确率			0.87

模型算法	隐含层数	隐含层神经元数	学习率	MAE	RMSE
GA-LSTM	2	64/103	0.013 3	0.156	0.395
FPA-LSTM	2	42/88	0.026 1	0.134	0.366
混沌FPA-LSTM	2	68/80	0.017 5	0.126	0.355

分类算法	决策准确率	算法计算时间/s
DT	0.74	18.33
KDE-LPP-SVM	0.92	103.76
混沌FPA-LSTM	0.87	23.59

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