基于模糊推理系统的船舶操纵运动建模

Ship Maneuvering Motion Modeling Based on Fuzzy Inference System

基于典型的船舶操纵运动试验(Z型试验)的实船数据,通过运用数据挖掘技术提取模糊规则,架构模糊推理系统,对船舶操纵运动系统进行建模和预报。为提高预测精度,利用调整模糊集区间划分的方法优化模糊推理系统,使模糊推理系统的结构与样本数据分布相适应。实船操纵运动时间序列数据预报的仿真结果证实了所提出的基于数据挖掘技术的模糊推理系统的实用性和有效性。支持度与信任度2个数据挖掘概念的引入能帮助模糊推理系统从实际采集数据中提取模糊规则,对实现复杂系统的智能建模与控制具有重要意义。

In order to model the ship motion,a set of typical ship maneuvering motion data is collected from ship zig-zag tests. The ship maneuvering motion is modeled and forecast by the fuzzy inference system constructed with data mining technology. To improve the accuracy,the fuzzy inference system is optimized with a partition refining strategy,which can adapt the fuzzy inference system to the data set. The simulation of actual time serial data in ship maneuvering motion proves that the fuzzy inference system based on the data mining technology is a useful and effective method. The concepts from the data mining,the support degree and the confidence degree are introduced,which is essential in extracting fuzzy rules from numerical data for fuzzy modeling and control.