基于BP神经网络优化遗传算法的智能座舱感性意象预测

doi:10.19562/j.chinasae.qcgc.2023.08.018

Abstract

Abstract:

In order to reduce subjective interference and meet the diverse emotional needs of users， the design method of intelligent cockpit perceptual image prediction based on BP neural network optimization genetic algorithm is proposed. From the user's point of view， user emotional image is obtained and the intensity is divided. Factor analysis method is used to reduce dimension to obtain target images and cockpit samples of new energy vehicles. Cluster analysis method is applied to screen and obtain advantage samples and the modeling characteristic factors of intelligent cockpit central control are extracted by combining with morphological analysis method. Based on BP neural network， the mapping model of specific target image and modeling feature factors is constructed， and the functional relationship between the two is obtained， which is used as fitness function to carry out genetic algorithm analysis， optimize the optimal combination of modeling factors under the specific image， and complete the combination of evaluation method and optimization method. According to the combination of advantage factors， the design practice is carried out to verify the practicability of the method. The results show that the method can effectively meet the multidimensional emotional needs of users， and provide a new idea and reference for the diversification of intelligent cockpit modeling design.

Key words: intelligent cockpit modeling optimization, perceptual image prediction, BP neural network, genetic algorithm

Guoqiang Chen,Zhengyi Shen,Li Sun,Mengfan Zhi,Tong Li. Intelligent Cockpit Perceptual Image Prediction Based on BP Neural Network Optimization Genetic Algorithm[J].Automotive Engineering, 2023, 45(8): 1479-1488.

Figures/Tables 27

训练样本	造型特征
训练样本	W₁	W₂	W₃	W₄	W₅
A₁	000001	010000	010000	010000	000001
A₂	010000	001000	100000	100000	100000
A₃	000001	000010	000001	000000	000001
$?$	$?$	$?$	$?$	$?$	$?$
A₃₀	000001	001000	000001	001000	001000

训练样本	意象词汇均值
训练样本	C₁	C₃	C₁₃	C₂₂
A₁	0.07	-0.07	0.93	-0.86
A₂	0.57	0.14	0.71	0
A₃	1.07	-0.14	-0.21	0.5
A₄	0.86	-0.5	0.43	0
A₅	-0.43	0.57	0.14	0.21
$?$	$?$	$?$	$?$	$?$
A₂₉	0.86	0.07	0.93	0.29
A₃₀	0.93	0.07	0.57	0.64

预测样本	意象词汇评价均值
预测样本	C₁	C₃	C₁₃	C₂₂
$A 1 *$	0.107 1	-0.082 0	1.230 0	-0.802 1
$A 2 *$	0.624 0	0.125 7	0.980 0	0.006 0
$A 3 *$	0.873 4	-0.184 6	-0.321 0	0.481 0
$A 4 *$	0.840 0	-0.653 4	0.389 0	0.009 0
$A 5 *$	-0.502 0	0.701 0	0.098 9	0.310 0
$?$	$?$	$?$	$?$	$?$
$A 29 *$	0.824 0	0.090 0	0.867 2	0.290 1
$A 30 *$	0.976 0	0.040 0	0.590 1	0.613 0

相对误差值	意象词汇评价均值
相对误差值	C₁	C₃	C₁₃	C₂₂
A₁与 $A 1 *$	0.530 0	0.171 4	0.322 6	0.067 3
A₂与 $A 2 *$	0.094 7	0.102 1	0.380 3	0.000 0
A₃与 $A 3 *$	0.183 7	0.318 6	0.528 6	0.038 0
A₄与 $A 4 *$	0.023 3	0.306 8	0.095 3	0.000 0
A₅与 $A 5 *$	0.167 4	0.229 8	0.293 6	0.476 2
$?$	$?$	$?$	$?$	$?$
A₂₉与 $A 29 *$	0.041 9	0.285 7	0.067 5	0.000 3
A₃₀与 $A 30 *$	0.049 5	0.428 6	0.035 3	0.042 2

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序号	序号	序号	序号	序号
A₁	A₇	A₁₃	A₁₉	A₂₅
A₂	A₈	A₁₄	A₂₀	A₂₆
A₃	A₉	A₁₅	A₂₁	A₂₇
A₄	A₁₀	A₁₆	A₂₂	A₂₈
A₅	A₁₁	A₁₇	A₂₃	A₂₉
A₆	A₁₂	A₁₈	A₂₄	A₃₀

阶段	系数	阶段	系数	阶段	系数	阶段	系数
1	37.486	9	64.650	17	87.677	25	204.847
2	40.949	10	67.208	18	87.908	26	217.351
3	46.495	11	67.349	19	90.602	27	248.490
4	47.727	12	68.838	20	98.128	28	265.370
5	48.681	13	71.098	21	98.527	29	269.261
6	48.728	14	74.981	22	102.683
7	52.491	15	81.369	23	107.615
8	64.322	16	86.063	24	114.711

个案号	编号	聚类	距离	个案号	编号	聚类	距离	个案号	编号	聚类	距离
1	A₁	1	5.652	11	A₁₁	4	6.736	21	A₂₁	3	4.968
2	A₂	2	4.489	12	A₁₂	6	5.120	22	A₂₂	6	6.361
3	A₃	2	4.504	13	A₁₃	5	4.934	23	A₂₃	4	5.952
4	A₄	1	6.309	14	A₁₄	4	7.411	24	A₂₄	4	6.225
5	A₅	5	6.108	15	A₁₅	6	5.907	25	A₂₅	3	6.642
6	A₆	6	5.565	16	A₁₆	2	3.995	26	A₂₆	4	5.506
7	A₇	3	5.928	17	A₁₇	4	6.617	27	A₂₇	1	6.145
8	A₈	1	4.242	18	A₁₈	1	6.300	28	A₂₈	5	5.639
9	A₉	3	3.740	19	A₁₉	2	4.714	29	A₂₉	4	6.364
10	A₁₀	1	4.302	20	A₂₀	4	7.556	30	A₃₀	2	6.414

序号	样本	序号	样本	序号	样本
A₈		A₉		A₁₂
A₁₃		A₁₆		A₂₆

序号	意象词汇对	序号	意象词汇对	序号	意象词汇对
C₁	现代—传统	C₈	精致—粗犷	C₂₂	简洁—繁琐
C₃	圆润—硬朗	C₁₁	轻快—笨重	C₂₆	轻松—严肃
C₄	立体—平面	C₁₃	整体—分散
C₆	结实—脆弱	C₁₆	大气—小巧

Intelligent Cockpit Perceptual Image Prediction Based on BP Neural Network Optimization Genetic Algorithm

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Figures/Tables 27

References 20

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意象词汇对	A₂	A₉	A₁₀	A₁₂	A₁₃	A₂₆
C₁	0.2	0.7	-0.8	1.5	1.7	0.9
C₃	-1.8	0.3	1.0	-1.7	1.5	0.7
C₄	-0.4	-0.3	-0.7	1.3	1.6	0.5
C₆	0.8	0.7	0.5	0.8	0.9	1.7
C₈	0.6	0.6	-0.1	1.5	1.6	1.1
C₁₁	-0.1	1.2	-0.5	0.9	0.6	0.3
C₁₃	1.2	0.6	0.7	0.7	0.8	0.9
C₁₆	-0.1	-0.3	-0.6	0.1	1.5	1.0
C₂₂	0.4	0.9	-0.1	0.1	1.3	-0.1
C₂₆	-0.3	0.6	-0.6	0.1	1.8	-0.4

成分	提取载荷平方和
成分	总计	方差	累积
1	4.991	49.910	49.910
2	2.107	21.075	70.984
3	1.404	14.036	85.020
4	1.046	10.455	95.476

意象特征因子	成分
意象特征因子	1	2	3	4
C₁	0.957	0.223	-0.163	-0.092
C₃	-0.036	0.194	-0.184	0.963
C₄	0.908	0.168	-0.105	0.051
C₆	0.609	-0.577	0.260	0.349
C₈	0.979	0.131	-0.030	-0.083
C₁₁	0.593	0.255	-0.623	-0.236
C₁₃	0.043	-0.098	0.956	-0.237
C₁₆	0.831	0.128	0.270	0.468
C₂₂	0.208	0.940	-0.069	0.174
C₂₆	0.538	0.797	-0.163	0.219

造型特征W	造型特征因子
转向盘W₁
转向盘W₁	W₁₁	W₁₂	W₁₃	W₁₄	W₁₅	W₁₆
中控台W₂
中控台W₂	W₂₁	W₂₂	W₂₃	W₂₄	W₂₅	W₂₆
出风口W₃
出风口W₃	W₃₁	W₃₂	W₃₃	W₃₄	W₃₅	W₃₆
仪表盘W₄
仪表盘W₄	W₄₁	W₄₂	W₄₃	W₄₄	W₄₅	W₄₆
中控屏W₅
中控屏W₅	W₅₁	W₅₂	W₅₃	W₅₄	W₅₅	W₅₆

设计方案	评价得分
设计方案	C₁	C₃	C₁₃	C₂₂
方案a	2.13	0.33	-0.62	1.06
方案b	-1.55	2.95	-0.81	0.78
方案c	-1.42	-1.91	2.02	0.08
方案d	1.88	-0.89	0.27	2.54

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造型因子
造型因子	4	2	6	3	4
因子组合

意象特征C	编码	最优解
C₃	53326
C₁₃	66212
C₂₂	14456