摘要： 在射频识别的调制识别方法中，神经网络常用的反向传播算法普遍存在收敛速度慢、容易陷入局部极小点、网络参数的选取只能凭实验和经验确定等缺点。针对上述问题，提出一种基于遗传算法优化小波神经网络的识别分类器。该分类器可以充分发挥遗传算法的全局寻优能力、小波分析的非线性逼近能力和神经网络的自学习特性，仿真结果表明其可以优化系统的收敛速度和识别精度。

关键词: 遗传小波神经网络, 射频识别, 调制识别

Abstract: In recognition method of RFID modulation, the main disadvantage of the back propagation algorithm of neural network commonly used lies in the slow convergence speed, the optimization procedure getting easily stacked into the minimal value locally and network parameter decided by experiment and experience. This paper designs a recognition classifier of Genetic Algorithm-Wavelet Neural Network(GA-WNN), which has global optimization capability of GA, non-linear approximation ability of wavelet and self-learning characteristic of neural network, and simulation result proves that it can improve the recognition accuracy and convergence rate.

Key words: genetic wavelet neural network, RFID, modulation recognition

中图分类号: 

• TN911.7