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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Fuzzy Space Partitioning Based on Hyperplanes Defined by Eigenvectors for Takagi-Sugeno Fuzzy Model Identification
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Fuzzy Space Partitioning Based on Hyperplanes Defined by Eigenvectors for Takagi-Sugeno Fuzzy Model Identification

机译：基于由特征向量定义的超级预测的模糊空间分区，用于Takagi-sugeno模糊模型识别

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摘要

This article presents a novel method for fuzzy space partitioning and the identification of Takagi-Sugeno fuzzy models. The novelty is in its region-splitting mechanism and membership function definition, which is based on hyperplanes. The proposed algorithm introduces a concept of principal component analysis to define the hyperplanes that split the problem space and uses the distances to these hyperplanes as metrics instead of center-oriented clusters. In contrast with many other methods, the presented method delivers reproducible results and has an easy tuning procedure. The performance is illustrated with analytical examples, benchmark problems from the literature, and real-process data. The obtained results are very promising; however, as with most learning methods, the results depend on the data distribution and input variable selection.
机译：本文介绍了一种新颖的模糊空间分区方法，识别Takagi-Sugeno模糊模型。新颖性是其区域分裂机制和成员函数定义，基于超平面。该算法介绍了主成分分析的概念，以定义拆分问题空间的超平面，并使用与这些超平面的距离为度量而不是以中心为导向的集群。与许多其他方法相比，所呈现的方法可提供可重复的结果并具有易于调整过程。分析示例，来自文献的基准问题和实际过程数据的性能。获得的结果非常有前途;但是，与大多数学习方法一样，结果取决于数据分布和输入变量选择。

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