摘要
为了提高现行模糊辨识方法的有效性,提出了基于移动率的T-S模糊模型的结构辨识方法。主要工作如下:首先,定义T-S模糊模型的S型、Z型和梯形隶属函数的移动率,将此移动率与现行的隶属度相比较可以看出,提出的方法比较有效;然后,定义基于移动率的T-S模糊推理方法,并且提出基于移动率的前提和结论部分的T-S模型的辨识方法;最后,将提出的识别方法应用于降水量和安全形势的预测模糊建模。测试结果表明,与现行方法和模糊神经网络算法相比,该方法明显提高了模糊辨识的有效性,减少了规则数目,并降低了辨识误差。
To improve the effectiveness of the existing fuzzy identification method,a structure identification method based on moving rating was proposed for T-S fuzzy model.The main work is as below.Firstly,the moving rates for S-type,Z-type and trapezoidal membership functions of T-S fuzzy model were defined,and compared with proposed moving rate and the existing grade of the membership function,the proposed moving rate is more effective.Next,T-S fuzzy reasoning method based on moving rating was proposed,and the identification methods for premise and consequence based on moving rate were proposed for T-S model.Finally,the proposed identification method was applied to the fuzzy modeling for the precipitation forecast and security situation prediction.Test results,compared with existing method and fuzzy neural network algorithm,show that the proposed method significantly improves the effectiveness of fuzzy identification,and reduces the number of rule and identification error.
出处
《计算机科学》
CSCD
北大核心
2012年第11期170-173,182,共5页
Computer Science
基金
国家自然科学基金项目(60970157)
辽宁省博士启动基金项目(2081019)资助
关键词
模糊建模
结构辨识
模糊推理
降水量预测
安全态势
Fuzzy modeling
Structure identification
Fuzzy reasoning
Precipitation forecast
Security situation
