scholarly journals Nonparametric modeling of magnetorheological elastomer base isolator based on artificial neural network optimized by ant colony algorithm

2015 ◽  
Vol 26 (14) ◽  
pp. 1789-1798 ◽  
Author(s):  
Yang Yu ◽  
Yancheng Li ◽  
Jianchun Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Magnetorheological Elastomer ◽  
Nonparametric Modeling ◽  
Base Isolator ◽  
Artificial Neural

scholarly journals An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator

Algorithms ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 195
Author(s):  
Shiping Zhao ◽  
Yong Ma ◽  
Dingxin Leng
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Vibration Control ◽  
Back Propagation ◽  
Structural Vibration ◽  
Neural Network Modeling ◽  
Back Propagation Algorithm ◽  
Magnetorheological Elastomer ◽  
Power Cost ◽  
Artificial Neural

Recently, magnetorheological elastomer (MRE) has been paid increasingly attention for vibration mitigation devices with the benefits of low power cost, fail safe performances, and fast responses. To make full use of the striking advantages of MRE device, a highly precise model should be developed to predict its dynamic performances. In the work, an MRE isolator in shear–squeeze mixed mode is developed and tested under dynamic loadings. The nonlinear performances in various displacement amplitude and currents are shown. An artificial neural network model with a back-propagation algorithm is proposed to characterize the nonlinear hysteresis of MRE isolator for its implementation in vibration control applications. This model utilized the displacement, velocity, and applied current as inputs and output force as output. The results show that the proposed model has high modeling accuracy and can well portray the complicated behaviors of MRE isolator with different excitations, which shows a fundamental basis for structural vibration control.

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