Simulation Analysis of Active Vibration Reduction of Piezoelectric Intelligent Rotor Based on BP Neural Network

2021 ◽  
Vol 10 (01) ◽  
pp. 48-57
Author(s):  
丹 张
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Vibration Reduction ◽  
Active Vibration

scholarly journals Research on Fault Diagnosis of Hydraulic System of Fast Erecting Device Based on Fuzzy Neural Network

2022 ◽  
Author(s):  
Yangbing Zheng ◽  
Xiao Xue ◽  
Jisong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Hydraulic System ◽  
Fuzzy Neural Network ◽  
Simulation Analysis ◽  
Fuzzy Relation ◽  
Analysis Procedure ◽  
Fuzzy Neural ◽  
Simulation Results

In order to improve the fault diagnosis effectiveness of hydraulic system in erecting devices, the fuzzy neural neural network is applied to carry out fault diagnosis of hydraulic system. Firstly, the main faults of hydraulic system of erecting mechanism are summarized. The main faults of hydraulic system of erecting devices concludes abnormal noise, high temperature of hydraulic oil of hydraulic system, leakage of hydraulic system, low operating speed of hydraulic system, and the characteristics of different faults are analyzed. Secondly, basic theory of fuzzy neural network is studied, and the framework of fuzzy neural network is designed. The inputting layer, fuzzy layer, fuzzy relation layer, relationship layer after fuzzy operation and outputting layer of fuzzy neural network are designed, and the corresponding mathematical models are confirmed. The analysis procedure of fuzzy neural network is established. Thirdly, simulation analysis is carried out for a hydraulic system in erecting device, the BP neural network reaches convergence after 600 times iterations, and the fuzzy neural network reaches convergence after 400 times iterations, fuzzy neural network can obtain higher accuracy than BP neural network, and running time of fuzzy neural network is less than that of BP neural network, therefore, simulation results show that the fuzzy neural network can effectively improve the fault diagnosis efficiency and precision. Therefore, the fuzzy neural network is reliable for fault diagnosis of hydraulic system in erecting devices, which has higher fault diagnosis effect, which can provide the theory basis for healthy detection of hydraulic system in erecting devices.

scholarly journals Springback Study on Profile Flexible 3D Stretch-Bending Process Using the Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Yi Li ◽  
Ce Liang ◽  
Xiangfeng Lin ◽  
Jicai Liang ◽  
Zhongyi Cai ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Material Properties ◽  
Simulation Analysis ◽  
Orthogonal Experiment ◽  
Range Analysis ◽  
Finite Element Software ◽  
Stretch Bending ◽  
Aluminum Profile ◽  
Bending Process

The springback is one of the main defects in the flexible 3D stretch-bending process. In this paper, according to the orthogonal design of experiments, the numerical simulation analysis of the springback for the 3D stretch-bending aluminum profile is carried out by the ABAQUS finite element software. And to investigate the effect of material properties on the springback, the range analysis of the orthogonal experiment is performed. The results show that these material properties of the aluminum profile (elastic modulus E, yield strength σy, and tangent modulus E1) might have the biggest influence on the springback of the aluminum profile, and the optimized forming parameters are founded as follows: the horizontal bending degree is 14°, the vertical bending degree is 14°, the number of multipoint stretch-bending dies is 10, the friction coefficient is 0.15, and aluminum alloy grade is 6063. Moreover, the model of the BP neural network for the prediction of the springback is established and trained based on the orthogonal experiment, and the results with the BP neural network model are in good agreement with experimental results. So it is obvious that the BP neural network could predict effectively the springback of 3D multipoint stretch-bending parts.

Research on the PID Control Strategy in BP Neural Network of Terrain Detector-Milling Cutting Depth System

2014 ◽  
Vol 945-949 ◽  
pp. 1573-1578
Author(s):  
Xiao Feng ◽  
Hao Hu ◽  
Fan Rang Kong ◽  
Shi Qiu ◽  
Ye Sun
Keyword(s):  
Neural Network ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Pid Control ◽  
Simulation Analysis ◽  
Dynamic Performance ◽  
Effective Control ◽  
Cutting Depth ◽  
Hydraulic Servo

Targeting at the nonlinear, time-varying characteristics of terrain detector-milling cutting depth electro-hydraulic servo system in soil milling collection machines, this paper proposed the PID control menthod in BP neural network of terrain detector - milling cutting depth system and designed PID controller in BP neural network and conducted simulation analysis by programming with Matlab. The results show that, when compared with conventional PID control, BP neural network compounded with PID control would enable the system better dynamic performance and follow-up characteristics, therefore, it is an effective control strategy.

Research of Construction Subcontracting Enterprise Competence Based on GA-BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 2664-2667
Author(s):  
Xi Kang Yan ◽  
Jing Yu Wang
Keyword(s):  
Neural Network ◽  
Quantitative Analysis ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Matlab Simulation ◽  
Five Dimensions ◽  
Enterprise Competence ◽  
Bp Neural Network Model

A new evaluation index system, which includes five dimensions is put forward to evaluate the competitiveness of construction subcontracting enterprise properly. Based on GA optimized BP neural network model,construction subcontracting enterprises’ competitiveness can be quantitative analysis systematically. Use of Matlab simulation analysis,research has shown that this system can well solve the problem of construction subcontracting enterprise competitiveness evaluation.

Demand Prediction of the Rarely Used Spare Parts Based on the BP Neural Network

2014 ◽  
Vol 519-520 ◽  
pp. 1513-1519 ◽  
Author(s):  
Hong Long Mao ◽  
Jun Wei Gao ◽  
Xi Juan Chen ◽  
Jin Dong Gao
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Spare Parts ◽  
Back Propagation Algorithm ◽  
Demand Prediction ◽  
Consumption Data ◽  
Bp Neural Network Model

For the rarely used spare parts, as the traditional predicting methods can't keep the high accurateness, the BP neural network is used to predict the rarely used spare parts demand. Firstly, the rarely used spare parts definition and its characteristics are given in this paper. Then the three layer BP neural network model is established, the back propagation algorithm is used as the learning algorithm. Finally, the rarely used spare parts-bus coupler consumption data is used for simulation analysis based on Guangzhou Subway line 3. The results show that the prediction is good.

Study on Machine Accuracy of the Serial-Parallel Machine Tool Based on the BP Neural Network

2009 ◽  
Vol 407-408 ◽  
pp. 140-145
Author(s):  
Xu Ming Pei ◽  
Jie Liu ◽  
Chao Zhang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Bp Neural Network ◽  
Error Compensation ◽  
Degrees Of Freedom ◽  
Simulation Analysis ◽  
Control Strategies ◽  
Parallel Machine ◽  
Machine Accuracy ◽  
Parallel Machine Tool

It were researched that the modeling methods of machine accuracy and the control techniques of the error compensation based on BP neural network(BPNN) for parallel machine tool(PMT)with five degrees of freedom(DOF). The samples are obtained to train the BP neural network which has good capacity for non- liner mapping, learning and generalization. The machine accuracy mathematics model is established for the error compensation, in order to study the nonlinear input and output problem of the parallel machine which difficultly modeling described. The trained neural network was applied to error compensation of PMT to realize modifying errors real-timely. Finally, simulation analysis was performed through the MATLAB software. The results expressed that the control strategies for error compensation were simple, efficient and practicable. Machine accuracy can be increased greatly after compensation.

Application of Support Vector Machine in the Mixed Circuit Fault Diagnosis

2013 ◽  
Vol 441 ◽  
pp. 413-416
Author(s):  
Qiang Pan ◽  
Huai Long Wang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Simulation Analysis ◽  
Circuit Simulation ◽  
Support Vector ◽  
Vector Machines ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis ◽  
Mixed Circuit

Proposed a mixed circuit fault diagnosis method based on support vector machines for the lower rate of mixed circuit fault diagnosis and diagnosis slower. The basic idea is the use of wavelet decomposition to extract the dynamic current of the mixed circuit, and re-integration of the SVM fault diagnosis. By PSPICE software and MATLAB software simulation analysis of the mixed circuit, simulation results show that this method can improve the convergence speed of the BP neural network, and can make the BP neural network is not easy to fall into local minimum value, so that the network has a better pattern recognition capability. This laid the foundation for the completion of a more rapid and accurate mixed circuit fault diagnosis.

A FUZZY NEURAL NETWORK FOR THE ACTIVE VIBRATION CONTROL OF A CENTRIFUGAL PENDULUM VIBRATION ABSORBER

2009 ◽  
Vol 20 (12) ◽  
pp. 1963-1979
Author(s):  
CHI-HSIUNG LIANG ◽  
PI-CHENG TUNG
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Active Vibration Control ◽  
Bp Algorithm ◽  
Vibration Absorber ◽  
Neural Network Controller ◽  
Network Controller ◽  
Centrifugal Pendulum Vibration Absorber ◽  
Active Vibration ◽  
Simulation Results

In this study, we develop a fuzzy back-propagation (BP) neural network controller for active vibration control of a centrifugal pendulum vibration absorber (CPVA). The fuzzy BP neural network controller systems can be viewed as a conventional fuzzy algorithm for coarse tuning. The BP algorithm can also be applied for fine tuning, in this case to regulate the anti-resonance frequency in an active pendulum vibration absorber (APVA), by suppressing vibration of the carrier. The dynamic model of the APVA was developed and simulated using MATLAB. In the simulation results, when the frequency of the disturbance changes, the outputs of the fuzzy BP neural network controller are used to determine an appropriate value for the torque of the active pendulum such that the vibration amplitude of the carrier is minimized. A comparison of the carrier vibration results for the CPVA, the fuzzy algorithm and the fuzzy BP algorithm is performed. The simulation results demonstrate the effectiveness of the proposed fuzzy BP neural network APVA for reducing the carrier vibrations.

Design of PID Temperature Control System Based on BP Neural Network

2014 ◽  
Vol 602-605 ◽  
pp. 1244-1247
Author(s):  
Zhi Yong Meng ◽  
Guo Qing Yu ◽  
Rui Jin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Simulation Analysis ◽  
Nonlinear Function ◽  
Temperature Control System ◽  
Bp Network ◽  
Pid Algorithm

Based on BP neural network PID controller has the ability to approximate any nonlinear function, can achieve real-time online tuning PID controller parameter . Through the system simulation analysis, simulation results show that the BP neural network tuning PID control than traditional PID algorithm and BP network algorithm has a greater degree of improvement, the system has better robustness and adaptability, its output can also achieve the desired control accuracy through online adjustments. Suitable for temperature control system.

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