System identification using neural network with parametric sigmoid function

M. Hasheminejad, J. Murata, K. Hirasawa

Research output: Contribution to conferencePaperpeer-review

Abstract

Nonlinear systems can be modeled by neural networks. However choice of suitable network architecture is the most important problem. And 'how to find the best activation function' is a persistent aspect of the architecture design. Here we have proposed a sigmoid function with one parameter which provides us not only the reduction of error bound but also the opportunity of obtaining better insight into the systems.

Original languageEnglish
Pages39-44
Number of pages6
Publication statusPublished - Dec 1 1994
EventProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94) - St. Louis, MO, USA
Duration: Nov 13 1994Nov 16 1994

Conference

ConferenceProceedings of the Artificial Neural Networks in Engineering Conference (ANNIE'94)
CitySt. Louis, MO, USA
Period11/13/9411/16/94

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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