Title

A Hybrid Modeling Technique For Partially-Known Systems Using Linear Regression And Neural Network

Abstract

In this paper a hybrid modeling and system identification method, combining linear least squares regression and artificial neural network techniques, is presented to model a type of dynamic systems which have an incomplete analytical model description. This approach in modeling nonlinear, partially-understood systems is particularly useful to the study of manufacturing processes, where the linear regression portion of the hybrid model is established using a known mathematical model for the process and the neural network is constructed using the residuals from the least squares regression, therefore ensuring a more precise process model for the specific machining setup, tooling selection, workpiece properties, etc. In this paper the method is mathematically proven to give regression coefficients close to those which would be found if only a regression had been performed. The modeling method is then simulated for a macro-scale hard turning process, and the result proves the effectiveness of the proposed hybrid modeling method. Copyright © 2009 by ASME.

Publication Date

12-1-2009

Publication Title

Proceedings of the ASME International Manufacturing Science and Engineering Conference 2009, MSEC2009

Volume

2

Number of Pages

365-375

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1115/MSEC2009-84217

Socpus ID

77953224712 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/77953224712

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