基于改进GNG算法的燃煤锅炉数据动态特征分析与控制

数据动态特征分析与控制技术是一种重要的数据挖掘手段,燃煤锅炉数据具有明显时序性和漂移性等特点,针对目前数据跟踪与监督算法大多缺乏动态性、实时性和稳定性等问题,设计一种基于改进生长神经气模型(GNG)的自适应聚类模型,建立基于概率、范围搜寻、节点平均距离的节点生成、删除机制,实现对漂移数据实时监控.最后以燃煤锅炉动态数据为分析对象进行实验,实验结果表明该模型和算法对动态漂移数据的实时跟踪能力更强,能对燃煤锅炉动态数据进行准确、有效的监督和控制.

Dynamic characteristics analysisand control of coal-fired boiler based on improved GNG algorithm

Data dynamic feature analysis and control technology are considered as important data mining methods. The coal-fired boiler data have obvious characteristics such as timing and drift. In view of the current lack of dynamic, real-time problems of data tracking monitoring algorithms, we propose an adaptive clustering model based on improved growing neural gas(GNG). The improved GNG is used to realize real time monitoring for drift data. The node generation and deletion mechanism are established. based on probability, range search and node average distance. Finally, the dynamic data for coal-fired boilers are taken as the analysis object. The experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data, and can effectively supervise and control the dynamic data of coal-fired boilers.