中文摘要：

认知网络能够感知外部环境,并能根据周围环境的变化智能、自主、自适应的动态变化,这种特性更适合为用户提供QoS（Quality of Service）保障.设计高精度的流量预测模型,可以提高认知网络的认知特性.本文针对原有预测模型预测精度低、对训练数据依赖程度高以及不能很好的刻画网络流量特征的不足,提出了一个混合的流量预测模型.它使用蚁群算法训练BP网络的权值,避免了梯度下降法收敛速度慢、容易陷入局部最优的问题.并且在预测之前,首先使用BP（Back Propagation）网络剔除原始数据中的异常数据信号,再对其进行小波分解,最后使用混合模型预测网络流量,实现了认知网络中高精度的流量预测.

英文摘要：

Cognitive networks can perceive the external environment,and intelligently and automatically change their behavior to adapt to the environment,so it is more suitable to provide users security with QoS.Designing a high-precision traffic prediction model can improve the cognitive feature of cognitive networks.For the models of low forecasting precision,highly independence to training samples and bad description of network traffic,we propose a new model.It trains BP with Ant Colony Algorithm,which avoids slow convergence speed and easily falling into local optimum problems existed in the gradient descent method.Besides,we reject the abnormal data using BP1,do wavelet decomposition,and predict the network traffic with hybrid model.Experimental results show its high-precision in cognitive networks.