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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