Optimum Design of a High Power Density Magnetic-Gel Brake Base Using Neural Network Model and Genetic Algorithm

Abstract:

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An integrated approach based on genetic algorithm (GA) and an artificial neural network (ANN) is presented for structural optimization of a high power density magneto-gel brake. For this method, the GA method is employed for obtaining the optimal configuration of the brake by minimizing the dimensions of the brake. Subsequently, a two-layer BP neural network model is trained to obtain the correlation between main design parameters and performance of the brake, and then it is used to predict the performance of the magnetic gel brake with high power density. The coupled method incorporating the ANN with GA can reduce substantially the computation time during optimizing brake performance. Meanwhile, the proposed method’s validity is demonstrated by comparisons between the experiment and existing data.

Info:

Periodical:

Advanced Materials Research (Volumes 102-104)

Edited by:

Guozhong Chai, Congda Lu and Donghui Wen

Pages:

184-188

DOI:

10.4028/www.scientific.net/AMR.102-104.184

Citation:

H. Z. Lv and X. C. Liang, "Optimum Design of a High Power Density Magnetic-Gel Brake Base Using Neural Network Model and Genetic Algorithm", Advanced Materials Research, Vols. 102-104, pp. 184-188, 2010

Online since:

March 2010

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Price:

$35.00

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