Design of the optimized neurocontroller for human control skill transfer in car driving = 인간의 차량 운전 제어기술 전달을 위한 최적화된 신경망 제어기의 설계

Cited 0 time in webofscience Cited 0 time in scopus
  • Hit : 263
  • Download : 0
A human is an expert in manipulation. We have acquired skills to perform dexterous operations based upon knowledge and experience attained over a long period of time. It is important in robotics to understand these human skills, and utilize them to bring about better robot control and operation. It is hoped that the neural net controller can be trained and organized by simply presenting human teaching data, which implicate human intention, strategy and expertise. One weak point, however, results from this implicit method of a system construction approach. It is needed to determine the size of neural net controller. Improper size may not only incur difficulties in training neural nets, e.g. no convergence, but also cause instability and erratic behavior in machines. Therefore, it is necessary to determine the proper size of neural net controller for human control transfer. In this paper, a new pruning method is developed, based on the penalty-term methods. This method makes the neural networks good for the generalization and reduces the retraining time after pruning weights/nodes.
Advisors
Lee, Ju-Jangresearcher이주장researcher
Description
한국과학기술원 : 전기및전자공학전공,
Publisher
한국과학기술원
Issue Date
2001
Identifier
165904/325007 / 000993275
Language
eng
Description

학위논문(석사) - 한국과학기술원 : 전기및전자공학전공, 2001.2, [ vi, 53 p. ]

Keywords

optimized neurocontroller; human skill transfer; 인간제어기술 전달; 최적화된 신경망 제어기

URI
http://hdl.handle.net/10203/37452
Link
http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=165904&flag=dissertation
Appears in Collection
EE-Theses_Master(석사논문)
Files in This Item
There are no files associated with this item.

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0