摘要
该文提出了一种基于人工神经网络和遗传算法的光纤陀螺温度漂移建模的方法,并采用混配的方法,对遗传算法进行了改进,克服了遗传算法中所存在的种群内过早收敛的缺点,极大地提高了预测算法的准确度。经实测数据验证,该方法具有较好预测的效果。
This paper presents a method of modeling the FOG Temperature Drift based on artificial neural network and genetic algorithm. A novel method named commingle copulation is used to overcome the shortcoming of converging too fast existing in species in genetic algorithm. The precision of the predicting algorithm is largely improved. The simulation result showed that method can be used to identify the temperature Drift model of FOG.
出处
《微计算机信息》
北大核心
2006年第04Z期273-275,共3页
Control & Automation
基金
国家863项目
关键词
光纤陀螺
温度漂移
混配
人工神经网络
遗传算法
Fiber Optic Gyroscope
Temperature Drift
Commingle copulation
Artificial neural network
Genetic algorithm
