基于ISCSO-BP神经网络模型的光纤陀螺温度补偿技术研究

doi:10.16182/j.issn1004731x.joss.25-0073

摘要/Abstract

摘要：

针对环境温度变化导致光纤陀螺(fiber optic gyro，FOG)输出精度显著受影响，引发零偏漂移、增大测量误差并限制其复杂环境应用精度的问题，构建了基于BP神经网络的温度补偿模型。为了改善神经网络的性能，对沙猫群优化算法(sand cat swarm optimization，SCSO)进行了改进，运用改进沙猫群优化算法(improved SCSO，ISCSO)对BP神经网络的权值和阈值进行优化，实验结果表明：利用ISCSO-BPNN温补模型对陀螺温度误差进行补偿，相较其他对比算法显著提升了零偏稳定性和整体补偿精度。

关键词: 光纤陀螺, 温度误差, 温度补偿, BP神经网络, 沙猫群优化算法

Abstract:

To address the issue that changes in ambient temperature significantly affect the output accuracy of the fiber optic gyro (FOG), which causes zero bias drift, increases measurement errors, and limits their application accuracy in complex environments, a temperature compensation model based on BP neural networks was proposed. To improve the performance of neural networks, the sand cat swarm optimization (SCSO) was improved, and the improved SCSO (ISCSO) was used to optimize the weights and thresholds of BP neural networks. Experimental results show that using the ISCSO-BPNN temperature compensation model to compensate for the gyro's temperature errors significantly improves the zero bias stability and overall compensation accuracy compared with other comparative algorithms.

Key words: fiber optic gyroscope, temperature error, temperature compensation, BP neural network, sand cat swarm optimization(SCSO)

中图分类号:

TP391.9

张志利,刘瑾,周召发等 . 基于ISCSO-BP神经网络模型的光纤陀螺温度补偿技术研究[J]. 系统仿真学报, 2025, 37(11): 2904-2917.

Zhang Zhili,Liu Jin,Zhou Zhaofa,et al . Research on Temperature Compensation Technology of Fiber Optic Gyroscope based on ISCSO-BP Neural Network Model[J]. Journal of System Simulation, 2025, 37(11): 2904-2917.

图/表 18

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测试函数信息

函数	函数名称	基准测试函数	维度	搜索空间	最优值
F1	Rosenbrock	$f 1 (x) = 100 (x 12 - x 2) 2 + (1 - x 1) 2$	2	[-2.048, 2.048]²	0
F2	Michalewicz	$f 2 (x) = 21.5 + x 1 s i n (4 π x 1) + x 2 s i n (20 π x 2)$	2	[-3.0, 12.1; 4.1, 5.8]	-38.85
F3	Shaffer F6	$f 3 (x) = 0.5 - s i n 2 x 12 + x 22 - 0.5 1 + 0.001 (x 12 + x 22) 2$	2	[-100, 100]²	1
F4	Camel	$f 4 (x) = (4 - 2.1 x 12 + x 14 3) x 12 + x 1 x 2 + (- 4 + 4 x 22) x 22$	2	[-3, 3; -2, 2]	-1.031 6
F5	Quartic	$f 5 (x) = ∑ i = 1 30 i x 4 + G a u s s (0,1)$	30	[-1.28, 1.28]³⁰	0
F6	Schwefel's Problem2.22	$f 6 (x) = ∑ i = 1 30 \| x i \| + ∏ i = 1 30 \| x i \|$	30	[-10, 10]³⁰	0
F7	Rastrigin	$f 7 (x) = 100 - ∑ i = 1 10 [10 c o s (2 π x i) - x i 2]$	10	[-5.12, 5.12]¹⁰	0

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参考文献 15

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函数	指标	SCSO	DBO	WOA	ZOA	ISCSO
F1	Mean	5.854 5×10^-4	5.967 8×10^-4	5.656 2×10^-4	6.462 0×10^-4	5.204 3×10^-4
	Std	3.049 87×10^-4	3.095 7×10^-4	3.223 8×10^-4	2.738 3×10^-4	2.934 8×10^-4
	Cr/%	100	100	100	99	100
	Iter	59.24	69.17	35.60	77.12	40.06
F2	Mean	-38.797 2	-38.735 3	-38.735 7	-38.425 1	-38.804 4
	Std	0.058 2	0.046 26	0.045 0	0.752 3	0.047 0
	Cr/%	54	5	4	50	58
	Iter	471.30	480.91	487.37	420.57	457.83
F3	Mean	0.997 5	0.997 5	0.996 7	0.997 5	0.997 7
	Std	1.395 4×10^-11	1.276 5×10^-11	3.744 3×10^-3	6.291 8×10^-12	7.148 6×10^-12
	Cr/%	0	0	0	0	0
	Iter	500	500	500	500	500
F4	Mean	-1.031 1	-1.031 1	-1.031 1	-1.031 63	-1.031 3
	Std	2.955 5×10^-4	2.844 3×10^-4	2.904 0×10^-4	2.781 0×10^-4	2.700 3×10^-4
	Cr/%	100	100	100	100	100
	Iter	8.69	8.61	13.37	6.92	7.28
F5	Mean	6.388 8×10^-4	1.223 3×10^-3	3.884 2×10^-3	5.932 3×10^-4	5.462 6×10^-4
	Std	3.750 5×10^-4	7.078 2×10^-4	3.916 8×10^-3	3.134 9×10^-4	2.840 8×10^-4
	Cr/%	97	46	31	100	100
	Iter	142.92	411.73	433.61	66.59	60.17
F6	Mean	5.083 4×10^-4	6.523 1×10^-4	7.947 3×10^-4	7.498 2×10^-4	4.576 9×10^-4
	Std	3.001 9×10^-4	2.485 0×10^-4	2.444 8×10^-4	2.717 6×10^-4	2.825 8×10^-4
	Cr/%	100	100	100	100	100
	Iter	20.61	47.67	103	20.31	8.04
F7	Mean	3.902 1×10^-4	3.953 3×10^-4	3.863 2×10^-4	2.654 8×10^-4	2.414 8×10^-4
	Std	3.297 4×10^-4	3.472 4 ×10^-4	3.196 4×10^-4	2.830 3×10^-4	2.520 9×10^-4
	Cr/%	100	89	94	95	100
	Iter	13.86	113.07	163.62	69.44	4.96

模型	补偿前	补偿后
BP	0.008 4	0.018 500 0
DBO-BP	0.008 4	0.005 219 0
SCSO-BP	0.008 4	0.004 298 5
ISCSO-BP	0.008 4	0.003 090 9

模型	补偿前	补偿后
BP	0.017 4	0.031 100 0
DBO-BP	0.017 4	0.006 608 9
SCSO-BP	0.017 4	0.005 313 2
ISCSO-BP	0.017 4	0.003 717 7

模型	补偿前	补偿后
BP	0.010 8	0.012 800 0
DBO-BP	0.010 8	0.005 521 2
SCSO-BP	0.010 8	0.004 875 4
ISCSO-BP	0.010 8	0.003 236 3

模型	补偿前	补偿后
BP	0.013 9	0.018 000 0
DBO-BP	0.013 9	0.006 188 3
SCSO-BP	0.013 9	0.005 642 1
ISCSO-BP	0.013 9	0.004 236 3