대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2000년도 하계학술대회 논문집 D
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- Pages.2949-2952
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- 2000
복잡한 도로 상태의 동적 비선형 제어를 위한 학습 신경망
A Dynamic Neural Networks for Nonlinear Control at Complicated Road Situations
- Kim, Jong-Man (Dept. of Electricity Changhung College) ;
- Sin, Dong-Yong (Dept. of Radial Rays Hanra College) ;
- Kim, Won-Sop (Dept. of Electricity Changhung College) ;
- Kim, Sung-Joong (Dept. of Electronical and Information Eng, Chonbuk University)
- 발행 : 2000.07.17
초록
A new neural networks and learning algorithm are proposed in order to measure nonlinear heights of complexed road environments in realtime without pre-information. This new neural networks is Error Self Recurrent Neural Networks(ESRN), The structure of it is similar to recurrent neural networks: a delayed output as the input and a delayed error between the output of plant and neural networks as a bias input. In addition, we compute the desired value of hidden layer by an optimal method instead of transfering desired values by back-propagation and each weights are updated by RLS(Recursive Least Square). Consequently. this neural networks are not sensitive to initial weights and a learning rate, and have a faster convergence rate than conventional neural networks. We can estimate nonlinear models in realtime by ESRN and learning algorithm and control nonlinear models. To show the performance of this one. we control 7 degree of freedom full car model with several control method. From this simulation. this estimation and controller were proved to be effective to the measurements of nonlinear road environment systems.
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