NONLINEAR DATA DEPENDENT SYSTEM FOR NONLINEAR RANDOM VIBRATION.

H. Y. Lai, S. H. Hsieh

Research output: Contribution to conferencePaper

Abstract

This paper presents a computational method for the identification of nonlinear dynamic systems through random vibration data. The discrete model used is referred to as the nonlinear autoregressive moving average (NLARMA) model which is a direct extension of the conventional time series model. The identification is carried out by a Nonlinear Data Dependent System (NLDDS) modeling strategy. The NLDDS method utilizes a statistical data classification system for nonlinear pattern recognition, and a model search procedure for parameter estimation. It is shown that the NLDDS approach leads to a satisfactory result. The method is applicable to many engineering systems. The paper concludes with an example of the nonlinear chatter process and a discussion of issues associated with this type of modeling.

Original languageEnglish
Pages375-381
Number of pages7
Publication statusPublished - 1987 Jan 1

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

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