数字信号调制识别的RBFNN分类器算法研究与实现

提出了一种新的用于数字信号调制识别的径向基函数神经网络（RBFNN）分类器算法。该算法采用减法聚类算法和最小均方算法实现了对隐含层中心点个数及位置和输出层权值系数的自适应训练。此算法能够综合考虑所有特征参量，能够在多维空间内找到最佳分界面；同时，解决了隐含层中心点个数及位置的盲目性和随机性的问题。仿真实验表明，在相同特征参量情况下，该算法能够有效提高正确识别率。

Research and implementation of RBFNN classification algorithm for digital signal modulation recognition

A new Radial Basis Function Neural Network(RBFNN) classification algorithm for the digital signal modulation recognition was proposed.Subtraction clustering algorithm and Least Mean Square(LMS) algorithm were used to adaptively train the number and location of the hidden layer center and the weight coefficient of output layer considering all the characteristic parameters,and the best interface in the multi-dimensional space could be found.Meanwhile,the blindness and randomness of the number and position of ...