中文摘要：

为混合基因优化的一条新奇途径( HGO-EAC )(去)与精英蚂蚁，殖民地( EAC )为通讯信号的自动调整识别被建议，通过哪个我们由引用精英策略改进基本蚂蚁殖民地算法并且为基因优化和精英蚂蚁殖民地介绍新熔化策略。这条途径被用来为调整作为分类器训练神经网络。模拟结果在添加剂白人 Gaussian 噪音(AWGN ) 上显示好性能隧道，与识别率甚至在象 5 dB 一样低的 signal-to-noise 比率特别为 CW 到达到 70% 。这条途径能为象 CW， 4ASK， 4FSK， BPSK，和 QAM16 那样的典型调整完成高识别率。测试结果证明它由完成更快的训练和更强壮的坚韧性比 BP 算法和基本蚂蚁殖民地算法有更好的性能。

英文摘要：

A novel approach （HGO-EAC） for hybrid genetic op-timization （GO） with elite ant colony （EAC） is proposed for the automatic modulation recognition of communication signals,through which we improve the basic ant colony algorithms by referencing elite strategy and present a new fusion strategy for genetic optimization and elite ant colony. This approach is used to train the neural networks as the classifier for modulation. Simula-tion results indicate good performance on an additive white Gaus-sian noise （AWGN） channel,with recognition rate reaching to 70% especially for CW even at signal-to-noise ratios as low as 5 dB. This approach can achieve a high recognition rate for the typical modulations such as CW,4ASK,4FSK,BPSK,and QAM16. Test result shows that it has better performance than BP algorithm and basic ant colony algorithms by achieving faster training and stronger robustness.