Structure identification of generalized adaptive neuro-fuzzy inference systems

被引：98

作者：

Azeem, MF ^{[1
]}

Hanmandlu, M

Ahmad, N

机构：

[1] Aligarh Muslim Univ, Dept Elect Engn, Aligarh 202002, Uttar Pradesh, India

[2] Indian Inst Technol, Dept Elect Engn, New Delhi 110016, India

来源：

IEEE TRANSACTIONS ON FUZZY SYSTEMS | 2003年 / 11卷 / 05期

关键词：

approximate fuzzy data model (AFDM); fuzzy clustering; fuzzy modeling; mountain clustering; structure identification;

D O I：

10.1109/TFUZZ.2003.817857

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a method to identify the structure of generalized adaptive neuro-fuzzy inference systems (GANFISs). The structure of GANFIS consists of a number of generalized radial basis function (GRBF) units, The radial basis functions are irregularly distributed in the form of hyper-patches in the input-output space. The minimum number of GRBF units is selected based on a heuristic using the fuzzy curve. For structure identification, a new criterion called structure identification criterion (SIC) is proposed. SIC deals with a trade off between performance and computational complexity of the GANFIS model. The computational complexity of gradient descent learning is formulated based on simulation study. Three methods of initialization of GANFIS, viz., fuzzy curve, fuzzy C-means in x x y space and modified mountain clustering have been compared in terms of cluster validity measure, Akaike's information criterion (AIC) and the proposed SIC.

引用

页码：666 / 681

页数：16

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