Identification of Structural System Parameters Using the Cascade-Correlation Neural Network

1994 ◽  
Vol 116 (4) ◽  
pp. 790-792 ◽  
Author(s):  
H. Allison Smith ◽  
J. Geoffrey Chase

The use of neural networks for structural system identification is receiving an increasing amount of attention through the research focused on structural control and intelligent systems. These systems require continuous monitoring and controlling of structural response; thus, on-line identification techniques are needed to provide real-time information about structural parameters. The Cascade-Correlation (Cascor) neural network is applied here to the structural system identification problem. The Cascor network utilizes a dynamic network architecture and a variable error threshold mechanism which facilitates training and can increase the network’s ability to generalize.

Author(s):  
Tadashi Kondo ◽  
◽  
Junji Ueno

The logistic Group Method of Data Handing (GMDH)-type neural network identifying a complex nonlinear system we propose is automatically organized using heuristic self-organization that is basic to GMDH algorithm. In this neural network, structural parameters such as the number of layers, the number of neurons per layer, useful input variables, and optimum neuron architectures are automatically determined using a prediction error criterion defined as Akaike’s Information Criterion (AIC) to produce an optimum neural network architecture suiting the complexity of the nonlinear system. In applying this neural network to the identification problem of the X-ray film characteristic curve, we found that modeling with such a neural network is more accurate than applying multiple regression analysis, a conventional neural network, and GMDH algorithm.


1996 ◽  
Vol 118 (2) ◽  
pp. 211-220 ◽  
Author(s):  
Ketao Liu ◽  
Robert N. Jacques ◽  
David W. Miller

This paper presents the Frequency Domain Observability Range Space Extraction (FORSE) identification algorithm. FORSE is a singular value decomposition based identification algorithm which constructs a state space model directly from frequency domain data. The concept of system identification by observability range space extraction was developed by generalizing the Q-Markov Covariance Equivalent Realization and Eigensystem Realization Algorithm. The numerical properties of FORSE are well behaved when applied to multi-variable and high dimensional structural systems. It can achieve high modeling accuracy by properly overparameterizing the system. The effectiveness of this algorithm for structural system identification is demonstrated using the MIT Middeck Active Control Experiment (MACE). MACE is an active structural control experiment to be conducted in the Space Shuttle middeck. Results of ground experiments using this algorithm will be discussed.


2017 ◽  
Vol 109 (7) ◽  
pp. 3254-3261
Author(s):  
Jun LEI ◽  
Dong XU ◽  
José Antonio Lozano-Galant ◽  
María Nogal ◽  
José Turmo

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