✦ LIBER ✦

NON-LINEAR SYSTEM IDENTIFICATION USING LUMPED PARAMETER MODELS WITH EMBEDDED FEEDFORWARD NEURAL NETWORKS

✍ Scribed by YIMIN FAN; C. JAMES LI

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 320 KB
Volume: 16
Category: Article
ISSN: 0888-3270
DOI: 10.1006/mssp.2001.1466

No coin nor oath required. For personal study only.

✦ Synopsis

This paper describes a new methodology to identify multi-degree-of-freedom non-linear systems from the system's operating data. The methodology includes a new non-linear model architecture which embeds feedforward neural networks to represent unknown nonlinearities in a lumped parameter model, and a learning algorithm to train the embedded neural networks as well as the other model parameters to obtain model fidelity. Three simulated and experimental examples are used to validate the proposed methodology.

📜 SIMILAR VOLUMES

NON-LINEAR MECHANICAL SYSTEMS IDENTIFICA

NON-LINEAR MECHANICAL SYSTEMS IDENTIFICATION USING LINEAR SYSTEMS WITH RANDOM PARAMETERS

✍ S. BELLIZZI; M. DEFILIPPI 📂 Article 📅 2003 🏛 Elsevier Science 🌐 English ⚖ 233 KB

A method to identify the parameters involved in the non-linear terms of randomly excited mechanical systems is presented. It is based on the minimisation of an index function which reflects the difference between an analytical approximation of the powerspectral density function response and the meas

Non-linear modelling with a coupled neur

Non-linear modelling with a coupled neural network — PLS regression system

✍ Greger Andersson; Peter Kaufmann; Lars Renberg 📂 Article 📅 1996 🏛 John Wiley and Sons 🌐 English ⚖ 485 KB 👁 2 views

In this work a methodology is presented for the transformation of non-linear response data via a neural network and subsequent standard linear PLS regression. The superb transparency of linear PLS is retained with respect to the diagnostic capabilities via residual analysis and leverage, thus making

IDENTIFICATION OF RESTORING FORCES IN NO

IDENTIFICATION OF RESTORING FORCES IN NON-LINEAR VIBRATION SYSTEMS USING FUZZY ADAPTIVE NEURAL NETWORKS

✍ Y.C. LIANG; D.P. FENG; J.E. COOPER 📂 Article 📅 2001 🏛 Elsevier Science 🌐 English ⚖ 260 KB

The fuzzy adaptive back-propagation (FABP) algorithm which combines fuzzy theory with arti"cial neural network techniques is applied to the identi"cation of restoring forces in non-linear vibration systems. Simulated results show that the FABP algorithm is e!ective for the identi"cation of dynamic s