基于孪生网络的仿真模型智能排序评估方法

doi:10.12305/j.issn.1001-506X.2023.07.16

Abstract

Abstract:

Aiming at ranking and selection of simulation models which dynamic outputs and multiple samples, an intelligent ranking evaluation method of simulation models based on siamese convolutional neural network(SCNN) is proposed. Firstly, the consistency measurement problem of simulation data and reference data is transformed into a feature consistency measurement problem. Secondly, based on the analysis of the characteristics of the evaluation data and the comparative test results, it is determined to use SCNN to achieve feature extraction of the evaluation data. Next, a ranking evaluation method for simulation models based on SCNN is presented, which includes three parts: preliminary design of network structure, training and optimization of network parameters, and ranking evaluation of simulation models. Finally, an example application verifies the effectiveness of the proposed method in evaluation data feature extraction and simulation models ranking and selection.

Key words: ranking evaluation of simulation models, siamese convolutional neural network (SCNN), multiple sample data, feature extraction

CLC Number:

TP391.9

Fan YANG, Ping MA, Wei LI, Ming YANG. Intelligent ranking evaluation method of simulation models based on siamese network[J]. Systems Engineering and Electronics, 2023, 45(7): 2060-2068.

Figures/Tables 15

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名称	尺寸	步进	输出维度
Conv_1	1×202	1	1×3 800×64
Pool_1	1×200	3	1×1 201×64
Conv_2	1×201	1	1×1 001×128
Pool_2	1×201	2	1×401×128
Full	-	-	200

孪生网络	子网络	实验结果	结果分析	是否适用
基本SNN	FC	损失值逐渐趋近于0;数据特征按照模型呈聚集状分布; 训练时间较长	网络结构中参数较多, 导致训练速度较慢, 迭代次数较多	是
SCNN	CNN	损失值逐渐趋近于0;数据特征按照模型呈聚集状分布; 训练时间较短	训练速度相对较快, 调参方便	是
SLSTM	LSTM	损失值下降到一定程度后不再下降; 相同输入条件下数据特征分布较为接近	具有记忆功能, 无法直接处理超长序列, 会导致训练时间过长、梯度消失等问题	否
SAE	AE	损失值几乎不收敛; 相同输入条件下数据特征分布较为接近	对于类内差异性较大的数据, 预训练过程容易导致过拟合	否

样本组别	输入条件	所属模型	标签
1~10	条件1	模型1	1
11~20	条件1	模型2	2
21~30	条件1	模型3	3
31~40	条件1	模型4	4
41~50	条件2	模型1	1
51~60	条件2	模型2	2
61~70	条件2	模型3	3
71~80	条件2	模型4	4
81~90	条件3	模型1	1
91~100	条件3	模型2	2
101~110	条件3	模型3	3
111~120	条件3	模型4	4
121	条件1	参考	-
122	条件2	参考	-
123	条件3	参考	-

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Intelligent ranking evaluation method of simulation models based on siamese network

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特征一致性排序	样本组别										输入条件	所属模型	模型排序
1~10	1	4	9	3	10	5	7	6	8	2	条件1
11~20	89	90	87	86	85	82	81	88	84	83	条件3	模型1	1
21~30	44	45	47	43	50	41	48	46	42	49	条件2
31~40	16	13	14	15	20	18	19	12	17	11	条件1
41~50	100	92	95	93	96	99	97	94	98	91	条件3	模型2	2
51~60	60	59	58	57	53	52	51	55	54	56	条件2
61~70	26	24	27	30	21	29	25	28	22	23	条件1
71~80	109	107	108	104	105	101	110	106	102	103	条件3	模型3	3
81~90	69	67	65	64	63	62	70	66	61	68	条件2
91~100	38	34	39	31	32	33	37	40	36	35	条件1
101~110	115	116	117	119	113	112	120	111	118	114	条件3	模型4	4
111~120	76	74	80	75	77	79	73	71	78	72	条件2