摘要
在前庭功能检查中,要对平稳跟踪信号进行分析,必须先对实验者的跟踪数据信号进行曲线拟合。由于BP神经网络可以实现输入和输出间的任意非线性,使得它在函数逼近上有广泛的应用。因此本文用BP神经网络对平稳跟踪信号进行曲线拟合。对网络的训练方法采用贝叶斯正则化算法。实验表明,拟合的曲线和正弦曲线基本吻合,尽可能的通过了样本点,也排除少数点的干扰。
In the vestibular function test, first, we should fit a curve for signals of tracking data ,and the we can analyse signals of smooth pursuit test . As the BP neural network can achieve any non-linear between the input and output, making it has a wide range of applications on the function approximation. Therefore, this paper use BP neural network to fit the signal. And we use Bayesian regularization algorithm as our training method on the network. Experiments show that the fitted curve is similar to the sine curve, and the curve through the sample points as much as possible, and also excluded the interference of a small number of points. So, it works well.
出处
《科技信息》
2010年第01X期52-53,共2页
Science & Technology Information
关键词
BP神经网络
平稳跟踪信号
贝叶斯正则化算法
BP neural network
Smooth pursuit test signal
Bayesian regularization algorithm
