MPSO算法优化BP网络的数字调制识别方法

数字调制信号的识别方法有很多，其识别效果不尽相同。为了提高数字调制信号在不同信噪比（Signal-to-Noise Ratio，SNR）下的识别性能，提出了一种基于改进粒子群（Modified Particle Swarm Optimization，MPSO）算法优化BP网络的识别方法。针对七种常见的数字调制信号，提取了六个瞬时特征参数，其中Rσa]参数是改进得到的，同理类推得到Rσp]。为了在保持基本粒子群（Particle Swarm Optimization，PSO）算法优点的基础上进一步提高算法的性能，增加了对粒子邻域信息的参考，再用MPSO算法优化BP网络的权值和阈值。从仿真实验可以看出，应用此方法，七种信号的识别率都可以达到86％以上，从而证明了该方法能有效地提高数字调制信号的识别性能。

Digital modulation recognition method based on MPSO optimizing BP neural network

Currently, many recognition methods of digital modulation signals occur, which have different recognition effects. In order to improve recognition performance of the signals under different Signal-to-Noise Ratio（SNR）, a method based on Modified Particle Swarm Optimization（MPSO） algorithm optimizing BP is proposed. Six instantaneous feature parameters are extracted, among which the Rσa] is an improvement and the Rσp] is an analogy of the Rσa]. The neighbor information of particles are referred to modify Particle Swarm Optimization（PSO） algorithm optimizing the weights and thresholds of BP. Simulation result shows that the method has a remarkable performance because the probability to recognize seven kinds of signals is over 86%.