Enforced fuzzy neural networks

被引：0

作者：

Chen, B.G. ^{[1
]}

Zhu, Y. ^{[1
]}

Zhang, H. ^{[1
]}

Zhang, J.Y. ^{[1
]}

机构：

[1] Dep. of Control Eng., Harbin Inst. of Technol., Harbin 150001, China

来源：

| 2001年 / Harbin Institute of Technology卷 / 33期

关键词：

Approximation theory - Function evaluation - Fuzzy sets - Learning systems - Nonlinear systems;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The general enforced fuzzy neural network (EFNN) is proposed to obtain higher accuracy of closing-in-nonlinear system with the consequent fuzzy rules given in the form of function, which determines that the structure of network is a combination of two sub-networks: character network and function one, where the parameters are tuned with the grade descending algorithms. Simulation results show that the network has a higher accuracy of closing-in-on-nonlinear system and a faster training speed.

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