联合模糊聚类和多样本SSO优化SVR的配电通信网络流

针对电力通信网络流量预测问题，提出了一种联合模糊聚类和多样本群居蜘蛛优化（social spider optimization，SSO）SVR的配电网络流量预测方案。针对配电网络流量时序非线性、周期性特点，首先采用自适应模糊聚类方法（adaptive fuzzy C-means clustering）对流量样本数据进行预处理，实现了样本数据聚类自动划分，有效降低了流量数据短相关性对预测精度的影响；然后利用多样本SSO优化算法（multi-sample social spider optimization algorithm）对SVR预测模型参数进行优化，通过引入多样本、网格迭代进化策略，从而得到不同流量数据聚类对应的最佳SVR参数组合；最后，运用多样本SSO优化SVR模型对预测数据进行预测分析。仿真结果表明，同ARIMA、神经网络等配电网路流量预测模型相比，提出的预测方案预测精度提高了18.8%~34.1%。

Prediction Method Based on FCM and Multi-Sample Social Spider Optimization SVR for Distribution Communication Network Traffic

Aiming at the prediction of distribution communication network traffic, a network traffic prediction algorithm based on fuzzy C-means clustering and multi-sample SSO optimization SVR is proposed. First, according to the nonlinear and periodic characteristics of network traffic time series, the adaptive fuzzy C-means clustering method is used to process the training traffic sample set, which can automatically divide the traffic sample data into different clusters and effectively reduce the influence of sample data difference for prediction performance. Second, the multi-sample social spider optimization algorithm is used to optimize the SVR prediction model parameters, through the introduction of multiple samples and mesh iterative evolution strategy, the best SVR parameters combination is obtained for different traffic data clusters. Finally, the improved SVR model is used to predict the test data. The simulation results show that, compared with ARIMA and the neural network prediction model, the prediction accuracy of the proposed me-thod is improved by 18.8% ~ 34.1%.