Neuro Identification for Flexible Cantilever Beam Structure


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Abstract


In this study, system identification of a flexible beam which undergoes a small deflection due to the disturbance force was conducted. Recursive Least Square (RLS) and several intelligent Neuro identifications were established to acquire the dynamic model of the beam based on One Step-Ahead (OSA) prediction. Comparative assessment between conventional and intelligent methods was investigated in this study with the aim to find the best model that can represents the flexible beam. Neural Network (NN) can be classified according to their structures and learning algorithms. For the purpose of this study, Focused Time-Delay Neural Network (FTDNN), Distributed Time-Delay Neural Network (DTDNN), Nonlinear Autoregressive with Exogeneous Variables (NARX), Elman NN and Radial Basis Function (RBF) were compared their effectiveness in emulating the dynamic behaviour of the system. The performance of the network was observed based on its capability to represents the system with the lowest Mean- Squared-Error (MSE) between the actual and predicted output responses of the models.
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Keywords


Recursive Least Square; Neural Network; Flexible Cantilever Beam; System Identification

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