从眼动仪提取数据，根据高斯核函数得到感兴趣区域-Extract data from the eye tracker, based on the Gaussian kernel function to get the area of ​ ​ interest

数据平滑函数，在高斯核函数的基础上进行的方法改进，-Data smoothing function, a method based on the Gaussian kernel function were improved,

数据平滑函数，在高斯核函数的基础上进行的改进-Data smoothing function, based on the Gaussian kernel function were improved

er than mean squared error (MSE) function only. As an additional merit, it is also revealed that rigorous Mercer kernel condition is not required in FKNN networks. When the proposed architecture of FKNN networks is constructed in a layer-by-lay