An embedded sigmoidal neural network for modeling of nonlinear systems

J. Hu, K. Hirasawa

Research output: Contribution to conferencePaper

2 Citations (Scopus)

Abstract

This paper discusses the problem of applying sigmoidal neural network to prediction and control of nonlinear dynamical systems. Instead of directly using neural networks as nonlinear models, we first develop a shield based on application specific knowledge, and then embed sigmoidal neural network model in the shield. An embedded sigmoidal neural network model obtained in this way not only has a structure favorable for certain applications such as controller design, but also has useful interpretation on part of model parameters. Corresponding to the meaningful part and the meaningless part of model parameters, a hierarchical training algorithm consisting of two learning loops is introduced to train the model, which has good performance on solving local minimum problems. The usefulness of the proposed prediction model is demonstrated by applying it to prediction and control of a simulated nonlinear system.

Original languageEnglish
Pages1698-1703
Number of pages6
Publication statusPublished - 2001 Jan 1
Externally publishedYes
EventInternational Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, United States
Duration: 2001 Jul 152001 Jul 19

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN'01)
CountryUnited States
CityWashington, DC
Period01/7/1501/7/19

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Hu, J., & Hirasawa, K. (2001). An embedded sigmoidal neural network for modeling of nonlinear systems. 1698-1703. Paper presented at International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, United States.