一种新的用于高度非线性系统建模和预测的转换输入域ANFIS

Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555) Pub Date : 2001-05-13 DOI:10.1109/CCECE.2001.933761

E. M. Abdelrahim, T. Yahagi

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引用次数: 21

摘要

在二维或多维系统中，样本数据的组成部分是强相关的，将输入空间划分为几个子空间而不考虑相关性是不合适的。在本文中，我们提出了使用主成分方法来消除输入空间或自适应神经模糊推理系统(ANFIS)中的任何冗余。这导致输入空间的有效划分到模糊模型，并显着减少建模误差。对三个常用的基准问题进行了计算机仿真，结果表明带有不相关过程的ANFIS比原始的ANFIS具有更好的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A new transformed input-domain ANFIS for highly nonlinear system modeling and prediction

In two or more-dimensional systems where the components of the sample data are strongly correlated, it is not proper to divide the input space into several subspaces without considering the correlation. In this paper, we propose the usage of the method of principal component in order to uncorrelate and remove any redundancy from the input space or the adaptive-neuro fuzzy inference systems (ANFIS). This leads to an effective partition of the input space to the fuzzy model and significantly reduces the modeling error. A computer simulation for three frequently used benchmark problems shows that ANFIS with the uncorrelation process performs better than the original ANFIS.

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Canadian Conference on Electrical and Computer Engineering 2001. Conference Proceedings (Cat. No.01TH8555)

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