Optimization in Fuzzy Flip-Flop Neural Networks

被引：0

作者：

Lovassy, Rita ^{[1
]}

Koczy, Laszlo T. ^{[1
]}

Gal, Laszlo ^{[1
]}

机构：

[1] Szechenyi Istvan Univ Gyor, Fac Engn Sci, Inst Informat Elect & Mech Engn, Gyor, Hungary

来源：

COMPUTATIONAL INTELLIGENCE IN ENGINEERING | 2010年 / 313卷

关键词：

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：

The fuzzy J-K and D flip-flops present s-shape transfer characteristics in same particular cases. We propose the fuzzy flip-flop neurons; single input-single output units derived from fuzzy flip-flops as sigmoid function generators. The fuzzy neurons-based neural networks, Fuzzy Flip-Flop Neural Networks (FNN) parameters are quasi optimized using a second-order gradient algorithm, the Levenberg-Marquardt method (LM) and an evolutionary algorithm, the Bacterial Memetic Algorithm with Modified Operator Execution Order (BMAM). The quasi optimized FNN's performance based on Dombi and Yager fuzzy operations has been examined with a series of test functions.

引用

页码：337 / 348

页数：12

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