基于高斯噪声干扰下的神经网络数据处理及误差分析

Neural Networks Data Processing and Error Analysis Based on Gaussian Noise Interference

首先对样本数据运用径向基函数的方法进行理想化插值处理,找到输入输出数据之间的函数关系t=h(x),在考虑高斯噪声干扰的情形下,构造新的能量函数,并使其收敛到稳定态,从而反推出精确插值函数t=y(x);由似然函数变形构造得到误差平方和函数E**作为误差分析函数,结果表明:E**的最小值点(或极小值点)即为似然函数的最大值点(或极大值点),所采用的噪声数据插值处理使得神经网络的整体误差最小。

This paper uses the method of RBF for interpolation of the data,and obtains the function t=h(x) based on input and output.A new energy function was constructed in considering the Gaussian noise,and the function was forced to converge to a steady state in order to launch anti-precision interpolation function t=y(x).According to the likelihood function,error function E** was received.The results revealed that the minimum point of E** was the maximum point of the likelihood function.The error caused by interpolation of data was the smallest of the neural network as a whole.