量子遗传算法和模糊神经网络结合的预测模型

Prediction Model Based on Quantum Genetic Algorithm and Fuzzy Neural Network

为解决网络流量时间序列的预测问题,针对传统模糊神经网络通常使用梯度下降法作为搜索算法容易陷入局部极小值的不足,文章提出了一种量子遗传算法与模糊神经网络相结合的网络流量时间序列预测模型。该算法利用量子遗传算法优化模糊神经网络的权值,对实际采集的网络流量时间序列进行建模。最后,预测结果表明模型具有较好的预测精度和效果。

In order to solve the prediction problem of network traffic time series, and aiming at the deficiency that traditional fuzzy neural networks usually uses gradient descent as a search algorithm to easily fall into the local minimum, this paper proposes a prediction model of network traffic time series based on quantum genetic algorithm and fuzzy neural network. This algorithm uses quantum genetic algorithm to optimize the weight of fuzzy neural network and to model the network traffic time series. Finally, the prediction results show that the model has better prediction accuracy and effect.