计算机科学 ›› 2012, Vol. 39 ›› Issue (11): 170-173.

• 人工智能 • 上一篇    下一篇

基于移动率的T-S模糊模型的结构辨识方法

李晶皎,许哲万,郭先日,李海朋   

  1. (东北大学信息科学与工程学院 沈阳110819);(金日成综合大学计算机科学大学 平壤}
  • 出版日期:2018-11-16 发布日期:2018-11-16

Structure Identification Method of T-S Fuzzy Model Method Based on Moving Rate

  • Online:2018-11-16 Published:2018-11-16

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模糊模型的结构辫识方法。主要工作如下: 首先,定义I=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子S模型的辫 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,并降低了辫识误差。

关键词: 模糊建模,结构辨识,模糊推理,降水量预测,安全态势

Abstract: 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 mov- ing 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 identi- fication, and reduces the number of rule and identification error.

Key words: Fuzzy modeling, Structure identification, Fuzzy reasoning, Precipitation forecast, Security situation

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