Complex-Valued Neuro-Fuzzy Inference System Based Classifier

被引：0

作者：

Subramanian, Kartick ^{[1
]}

Savitha, Ramaswamy ^{[1
]}

Suresh, Sundaram ^{[1
]}

Mahanand, B. S. ^{[2
]}

机构：

[1] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore

[2] Sri Jayachamarajendra Coll Engn, Dept Informat Sci & Engn, Mysore, Karnataka, India

来源：

SWARM, EVOLUTIONARY, AND MEMETIC COMPUTING, (SEMCCO 2012) | 2012年 / 7677卷

关键词：

Complex-valued neuro-fuzzy system; wirtinger calculus; classification; SEQUENTIAL LEARNING ALGORITHM; NETWORK; PREDICTION;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a complex-valued Takagi-Sugeno-Kang type-0 neuro-fuzzy inference system (CNFIS) and develop for it, a gradient-descent based learning algorithm to solve classification problems. The gradient-descent based learning algorithm is derived based on Wirtinger calculus: which preserves the amplitude-phase correlation. The performance of the developed algorithm is evaluated on a set of four binary classification problems and three multi-category classification problems. Comparison with various real-valued and complex-valued classifiers show the improved performance of CNFIS.

引用

页码：348 / 355

页数：8

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