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中文摘要：
针对通信信号具有非稳定和信噪比变化范围较大的特点，利用蚁群算法优化的神经网络分类器对各种调制信号的特征矢量进行分类识别，强化神经网络的广泛映射能力和蚁群算法的快速、全局收敛以及启发式学习等特点，避免神经网络收敛速度慢、易于陷入局部极小点的问题．使得分类器的识别率、收敛速度和鲁棒性得到明显改善，仿真实验中的信道为高斯信道，且在信噪比为5dB时也获得了较好的识别率．实验结果证明了此方法的有效性和可行性．
英文摘要：
This paper mainly proposes an algorithm that the optimal classifier of neural networks is implemented with ant colony algorithms, and automatically classes modulation types of communication signal. The method is according to the purpose of classification , using the advantages of non-linearity and adaptiveness of neural networks, and combining with the algorithms of fast training, robust and global convergence. It overcomes the drawbacks of the general classifier of neural networks. Computer simulations indicate good performance on an additive white Gaussian noise channel, even at signalto-noise ratios as low as 5 dB. This compares favorably with the performance obtained with most algorithms based on pattern recognition techniques.
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