Identification of nonlinear dynamic systems by using Probabilistic Universal Learning Networks

Kotaro Hirasawa, Jinglu Hu, Junichi Murata, Chun Zhi Jin, Kazuaki Yotsumoto, Hironobu Katagiri

研究成果: Paper査読

抄録

In this paper, a method for identifying nonlinear dynamic systems with noise is proposed by using Probabilistic Universal Learning Networks (PrULNs). PrULNs are extensions of Universal Learning Networks (ULNs). ULNs form a superset of neural networks and were proposed to provide a universal framework for modeling and control of nonlinear large-scale complex systems. But the ULN does not provide any stochastic characteristics of the signals propagating through it. The PrULNs are equipped with machinery to calculate stochastic properties of signals and to train network parameters so that the signals behave with the pre-specified stochastic properties. On the other hand, it is generally recognized that there exists an overfitting problem when identification of nonlinear dynamic systems with noise is done by neural networks. In this paper, it is shown from simulation results of identification of a nonlinear robot dynamics that PrULNs are useful for avoiding the overfitting.

本文言語English
ページ2123-2128
ページ数6
出版ステータスPublished - 1999 12 1
外部発表はい
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 1999 7 101999 7 16

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period99/7/1099/7/16

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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