Identification of nonlinear dynamic systems by using Probabilistic Universal Learning Networks

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

Research output: Contribution to conferencePaper

Abstract

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.

Original languageEnglish
Pages2123-2128
Number of pages6
Publication statusPublished - 1999 Dec 1
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: 1999 Jul 101999 Jul 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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  • Cite this

    Hirasawa, K., Hu, J., Murata, J., Jin, C. Z., Yotsumoto, K., & Katagiri, H. (1999). Identification of nonlinear dynamic systems by using Probabilistic Universal Learning Networks. 2123-2128. Paper presented at International Joint Conference on Neural Networks (IJCNN'99), Washington, DC, USA, .