Identification of nonlinear dynamic systems by using Probabilistic Universal Learning Networks

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherIEEE
Pages2123-2128
Number of pages6
Volume3
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999

Other

OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA
Period7/10/997/16/99

All Science Journal Classification (ASJC) codes

  • Software

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