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.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
本文提出了一种基于模式类特征空间统计分布的模糊隶属度函数模型，可有效地反映模式在特征空间中的真实分布，用于模式分类器输入特征的模糊化可获取更好的识别性能。
In this paper a model of discrete fuzzy membership function based on statistical distribution of features of pattern is presented. It is used for the fuzziness of input features of classifier.
应用推荐