中文摘要：

针对现有基于误差反向传播算法的多层感知器神经网络分类器在信号识别中存在收敛速度缓慢、出现假饱和现象等问题，采用蜂群算法提取信号的联合特征模块，提出快速支持、超级自适应误差反向传播、共轭梯度3种不同算法分别应用于多层感知器神经网络分类器，实现对通信信号的自动识别。所提算法和误差反向传播算法相比有更高的识别率。仿真结果表明，所提算法能够克服误差反向传播算法的缺陷，在隐藏层神经元仅为20个、信噪比为4dB条件下，3种算法的识别率均高于95％，且系统易于实现，在信号识别中具有广泛的应用前景。

英文摘要：

In view of the deficiencies, slow convergence and false saturation phenomenon, which are present in signal recognition of the existing multilayer perception neural network classifier based on backpropagation al gorithm, the combined feature module selected by a bee colony algorithm is used, and three different algo rithms, quick prop, super adapt error backpropagation and conjugate gradient, are presented and used in the multilayer perception neural network classifier to realize the automatic recognition of communication signals in this paper. There proposed algorithms have a higher recognition rate compard with the error backpropagation algorithm. The simulation results show that the proposed algorithms can overcome the shortcomings of the er ror back propagation algorithm, and the recognition rates are higher than 95% under the conditions that the number of neurons is only 20 in the hidden layer, the signal tonoise ratio of 4 dB, and the system is easy to re alization, and has wide application prospects in signal recognition.