Finite Element Model Updating Using Computational Intelligence Techniques

Author: Tshilidzi Marwala
Edition: 2010
Binding: Hardcover
ISBN: 1849963223

Finite Element Model Updating Using Computational Intelligence Techniques: Applications to Structural Dynamics

FEM updating allows FEMs to be tuned better to reflect measured data. Get Finite Element Model Updating Using Computational Intelligence Techniques computer books for free.
It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite Check Finite Element Model Updating Using Computational Intelligence Techniques our best computer books for 2013. All books are available in pdf format and downloadable from rapidshare, 4shared, and mediafire.

Finite Element Model Updating Using Computational Intelligence Techniques Free

It can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed t can be conducted using two different statistical frameworks: the maximum likelihood approach and Bayesian approaches. This book applies both strategies to the field of structural mechanics, using vibration data. Computational intelligence techniques including: multi-layer perceptron neural networks; particle swarm and GA-based optimization methods; simulated annealing; response surface methods; and expectation maximization algorithms, are proposed to facilitate the updating process. Based on these methods, the most appropriate updated FEM is selected, a problem that traditional FEM updating has not addressed. This is found to incorporate engineering judgment into finite

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