Self-learning structural identification algorithm

Tadanobu Sato; Makoto Sato

doi:10.1117/12.208287

8 May 1995 Self-learning structural identification algorithm

Tadanobu Sato, Makoto Sato

Proceedings Volume 2443, Smart Structures and Materials 1995: Smart Structures and Integrated Systems; (1995) https://doi.org/10.1117/12.208287
Event: Smart Structures and Materials '95, 1995, San Diego, CA, United States

Abstract

This paper deals with the identification of the dynamic characteristics of structural system. The relevant neural network characteristics of learning algorithm are discussed in the context of system identification. Because of self-learning nature of neural network the identified dynamic characteristics are strongly affected by the level of noise contained in the teaching signals. Using the Kalman filtering technique, a method to identify the dynamic characteristics of structural system proof against contaminating noise in teaching signals has been developed.

Citation Download Citation

Tadanobu Sato and Makoto Sato "Self-learning structural identification algorithm", Proc. SPIE 2443, Smart Structures and Materials 1995: Smart Structures and Integrated Systems, (8 May 1995); https://doi.org/10.1117/12.208287

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