网络微信信息预测模型优化设计仿真

Network WeChat Information Prediction Model Simulation Optimization Design

在对复杂网络中微信热点信息发现趋势预测模型研究过程中,微信热点信息一旦数据较大,会形成信息特征的不平稳性,信息形成混淆,采用当前算法建立的预测模型对网络中微信热点信息的混淆特征分解不够详细,无法精确反映微信热点信息发现趋势变化的特征,在建模过程中存在误差大的问题。提出采用改进灰色二阶算法的复杂网络中微信热点信息发现趋势预测建模方法。针对在复杂的网络中微信热点信息发现时间序列的非平稳特征,融合经验模式分解原理将其分解成等多个本征模式分量,利用灰色二阶算法组建相应的热点信息趋势平稳的时间序列预测模型,利用发现趋势原始数据提供的信息将模型中待辨识的趋势参数与边值进行非线性组合,并进行重新的辨识。仿真结果证明,利用灰色二阶算法建立的复杂网络中微信热点信息发现趋势预测模型建模精确度高。

In the complicated network WeChat hot information found trend prediction model of study process,WeChat hot information once the data is bigger,features not stability,will form the information confused information form,the current algorithm is adopted to establish the prediction model of confusing characteristics of WeChat hotspot in network information decomposition is not detailed,can not accurately reflect We Chat hot information found that the characteristics of the trend,existence of the problem of the great error in the process of modeling. Based on improved grey second-order algorithm WeChat hot information found in the complex network of trend prediction modeling method. For We Chat hotspot in complex network information discovery non-stationary characteristics of time series,fusion principle of empirical mode decomposition to break it down into several intrinsic mode functions,such as using the grey second-order algorithm to form the corresponding hot information trend stationary time series prediction model,using trend of raw data model of information will provide the trend of parameters to be identified in the combination with the nonlinear boundary value,and to identify. The experimental simulation proves that the grey second-order algorithm is used to establish We Chat hot information found in the complex network of trend prediction model of modeling accuracy is high.