Title :
Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network
Author :
Karam, M. ; Zohdy, Mohamed A.
Author_Institution :
Dept. of Electr. Eng., Tuskegee Univ., USA
Abstract :
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.
Keywords :
backpropagation; linear systems; nonlinear dynamical systems; pendulums; recurrent neural nets; back-propagation-based training; inverted pendulum; linearized model; model-based dynamic recurrent neural network; simple inverted pendulum; Approximation error; Computer networks; Mean square error methods; Modeling; Neural networks; Nonlinear dynamical systems; Nonlinear equations; Nonlinear systems; Recurrent neural networks; Systems engineering and theory;
Conference_Titel :
System Theory, 2005. SSST '05. Proceedings of the Thirty-Seventh Southeastern Symposium on
Print_ISBN :
0-7803-8808-9
DOI :
10.1109/SSST.2005.1460881
