非线性函数逼近作为统计理论的一个重要分支，在模式识别中有着广泛的应用。
As one of the important branches in statistic theory, the non-linear function has a large application in model-identification.
采用信号四阶和六阶统计量提取信号特征，使用新设计的误差函数训练RBF神经网络，使得识别的效率和正确度得到了明显的改善。
The forth-order and sixth-order cumulants of received signal are adopted for features extraction while RBF neural networks with a new designed training cost function being used for classifier.
相位统计矩方法利用MPSK信号符号初相位偶阶统计矩为M的单调递增函数的特性，并进行M进制识别复合假设检验。
In the SPM method the composite hypothesis test is performed by means of the monotonic characteristic of the even order statistical phase moments of MPSK signals.
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