【摘要】 神经网络可以很好的拟合任意的非线性函数。我们从 QRS波群的高频三维频谱中提取出一些定量的特征参数 ,用神经网络的方法对这些参数进行有监督的学习训练 ,最终能在由这些特征参数张成的 m维空间中构建出一个 m维的曲面来区分病人和健康人的 QRS波群高频三维频谱 ,从而使得训练后的网络能基于 QRS波群的高频三维频谱自行诊断出病人和健康人更多还原

【Abstract】 Neural networks can fit any nonlinear function. After drawing out several characteristic parameters from the three dimension spectrum for high frequency QRS waves, we input them into the network and trained the network. In this way, we can get a m dimension curved surface in the m dimension space which is constructed by those parameters, and this curved surface divides the space into two parts: the unhealthiness and the health. Now, the network can automatically distinguish between the healthiness and the unhealthiness according to their three dimension spectrum for high frequency QRS waves.更多还原