Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network

被引：5
|
作者：
Karam, M [1 ]
Zohdy, MA [1 ]
机构：
[1] Tuskegee Univ, Dept Elect Engn, Tuskegee, AL 36088 USA
来源：
Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory | 2005年
关键词：
modeling; recurrent neural networks; nonlinear systems;
D O I：
10.1109/SSST.2005.1460881
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A model-based dynamic recurrent neural network (MBDRNN) is used in this paper to improve the linearized model of a Simple Inverted Pendulum (SIP). The MBDRNN's equations start as those of the linearized SIP model. Then, through Back-Propagation-based training, the MBDRNN's activation functions' weights are modified with the objective of improving the linearized SIP model. Simulation results show that the MBDRRN effectively improved the linearized model. By tuning several of the MBDRNN parameters, an improved configuration was found yielding a satisfactory small modeling approximation error.
引用
收藏
页码：78 / 82
页数：5
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