| 引用本文: | 吴美平,胡小平.多层局部回归神经网络在激光陀螺捷联惯导系统惯性敏感器误差补偿中的应用.[J].国防科技大学学报,2001,23(6):104-108.[点击复制] |
| WU Meiping,HU Xiaoping.A Multi-layer Local Recurrent Neural Networks Applied to Compensation for Inertial Sensors' Errors of Laser Gyro SINS[J].Journal of National University of Defense Technology,2001,23(6):104-108[点击复制] |
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| 多层局部回归神经网络在激光陀螺捷联惯导系统惯性敏感器误差补偿中的应用 |
| 吴美平, 胡小平 |
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(国防科技大学 机电工程与自动化学院，湖南 长沙 410073)
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| 摘要: |
| 惯导系统误差补偿技术对提高武器装备的性能具有重要的意义，而误差补偿的关键在于误差模型的辨识。探讨将多层局部回归神经网络引入到惯性敏感器误差建模中，详细介绍了网络结构和对应的自适应动态梯度算法。仿真算例说明，多层局部回归神经网络在惯性敏感器输出误差建模时具有一定的优点：网络收敛速度快、较好的跟踪性能、稳定性好。 |
| 关键词: 捷联惯导系统 误差模型 多层局部回归神经网络 动态梯度算法 |
| DOI： |
| 投稿日期：2001-05-10 |
| 基金项目:国家部委预研基金资助 |
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| A Multi-layer Local Recurrent Neural Networks Applied to Compensation for Inertial Sensors' Errors of Laser Gyro SINS |
| WU Meiping, HU Xiaoping |
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(College of Mechatronics Engineering and Automation, National Univ. of Defense Technology, Changsha 410073, China)
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| Abstract: |
| It's important to improve the performance of the weapon by compensating for errors of inertial sensors. Identification of error model is the key of compensating for errors. A multi-layer local recurrent neural networks is adopted to model the errors of inertial sensors. The framework of networks and adaptable dynamic gradient arithmetic are presented in detail. The result of simulation example shows that multi-layer local recurrent neural networks has some advantage for modeling errors of inertial sensors' output: rapid convergence, good performance of tracking and stabilization. |
| Keywords: SINS errors model multi-layer local recurrent neural networks adaptable dynamic gradient arithmetic |
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