Optimal design of magnetorheological damper with multiple axial fluid flow channels using BP neural network and particle swarm optimization methodologies

Author(s):  
Guoliang Hu ◽  
Haonan Qi ◽  
Miao Chen ◽  
Lifan Yu ◽  
Gang Li ◽  
...  

In this paper, a magnetorheological (MR) damper with multiple axial fluid flow channels is developed to solve the conflicts between limitation of size dimension and improvement of damping performance. By setting symmetrical excitation coils at both ends of the MR damper, the effective fluid flow channels of the proposed MR damper are significantly lengthened. In order to investigate the distributions of magnetic flux lines and magnetic flux density of the MR damper, the finite element model of the MR damper is established by using ANSYS software. Moreover, an optimization method combining BP neural network and particle swarm optimization (PSO) is proposed to improve the magnetic field utilization of the designed damper, and the damping performances of initial and optimal MR dampers are also experimentally tested. The test results show that the output damping force of initial and optimal MR dampers is 3.13 kN and 5.98 kN respectively under the applied current of 1.8 A, increasing by 91.1%, and the dynamic adjustable range is 11.5 and 16.1 respectively, increasing by 40.0%. It is found that the damping performance of the proposed MR damper is significantly improved.

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 184656-184663
Author(s):  
Xiaoqiang Tian ◽  
Lingfu Kong ◽  
Deming Kong ◽  
Li Yuan ◽  
Dehan Kong

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.


2014 ◽  
Vol 511-512 ◽  
pp. 941-944 ◽  
Author(s):  
Hong Li Bian

Based on the particle swarm optimization (PSO) and BP neural network (BPNN), an algorithm for BP neural network optimized particle swarm optimization (PSOBPNN) is proposed. In the algorithm, PSO is used to obtain better network initial threshold and weight to compensate the defect of connection weight and thresholds of BPNN, thus it can make BPNN have faster convergence and greater learning ability. The efficiency of the proposed prediction method is tested by the simulation of the chaotic time series for Kent mapping. The simulations results show that the proposed method has higher forecasting accuracy compared with the BPNN, so it is proved that the algorithm is feasible and effective in the chaotic time series prediction.


2013 ◽  
Vol 765-767 ◽  
pp. 2805-2808
Author(s):  
Guo Wen Wang ◽  
Shi Xin Luo ◽  
Li He ◽  
Gang Yin

According to the question that BP Neural Network has slow velocity of convergence and is apt to fall into the minimum value, chaos thought is adopted in the particle swarm optimization (PSO). For this, chaos particle swarm optimization algorithm, which improve the ability of getting rid of fractional extreme point in the PSO, is presented and applied to the BP network exercise so that the calculation accuracy and velocity of convergence of BP network are increased. The method of training the BP network for speaker recognition, the recognition rate and speed of training have been greatly improved, making the speaker recognition based on BP neural network to get better results.


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