scholarly journals Macro Modeling of V-Shaped Electro-Thermal MEMS Actuator with Human Error Factor

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 622
Author(s):  
Dongpeng Zhang ◽  
Anjiang Cai ◽  
Yulong Zhao ◽  
Tengjiang Hu
Keyword(s):  
Neural Network ◽  
Simulation Model ◽  
Bp Neural Network ◽  
Human Error ◽  
Calculation Model ◽  
Fuzzy Mathematics ◽  
Ansys Simulation ◽  
Macro Modeling ◽  
Error Factor ◽  
Simulation Results

The V-shaped electro-thermal MEMS actuator model, with the human error factor taken into account, is presented in this paper through the cascading ANSYS simulation model and the Fuzzy mathematics calculation model. The Fuzzy mathematics calculation model introduces the human error factor into the MEMS actuator model by using the BP neural network, which effectively reduces the error between ANSYS simulation results and experimental results to less than 1%. Meanwhile, the V-shaped electro-thermal MEMS actuator model, with the human error factor included, will become more accurate as the database of the V-shaped electro-thermal actuator model grows.

Research on the hydrographic survey cycle for updating navigational charts

2021 ◽  
pp. 1-13
Author(s):  
Jing Duan ◽  
Xiaoxia Wan ◽  
Jianan Luo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Calculation Model ◽  
Impact Factors ◽  
Factors Affecting ◽  
Material Resources ◽  
Hydrographic Survey ◽  
Survey Planning ◽  
The Impact ◽  
The Relationship

Abstract Due to the vast ocean area and limited human and material resources, hydrographic survey must be carried out in a selective and well-planned way. Therefore, scientific planning of hydrographic surveys to ensure the effectiveness of navigational charts has become an urgent issue to be addressed by the hydrographic office of each coastal state. In this study, a reasonable calculation model of hydrographic survey cycle is established, which can be used to make the plan of navigational chart updating. The paper takes 493 navigational charts of Chinese coastal ports and fairways as the research object, analyses the fundamental factors affecting the hydrographic survey cycle and gives them weights, proposes to use the BP neural network to construct the relationship between the cycle and the impact factors, and finally establishes a calculation model of the hydrographic survey cycle. It has been verified that the calculation cycle of the model is effective, and it can provide reference for hydrographic survey planning and chart updating, as well as suggestions for navigation safety.

scholarly journals A modified NK algorithm based on BP neural network and DEMATEL for evolution path optimization of urban innovation ecosystem

2021 ◽  
Author(s):  
Ruijian Liu ◽  
Fangcheng Tang ◽  
Yuhan Wang ◽  
Shaofang Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Urban Governance ◽  
Innovation Network ◽  
Governance System ◽  
Innovation Ecosystem ◽  
Governance Capacity ◽  
New Era ◽  
Evolution Path ◽  
Simulation Results

AbstractIn the new era, the key measure to accelerate the construction of smart city, so as to promote the modernization of urban governance system and governance capacity, is to establish a good urban innovation ecosystem, and guide its continuous evolution to the direction of the highest efficiency and the best performance. Focusing on solving the practical problem of “how the urban innovation ecosystem evolves”, this paper develops a NK algorithm using BP neural network and DEMATEL method. First, through literature research, constructing the urban innovation ecosystem including five subsystems of innovation talents, innovation subjects, innovation resources, innovation environment and innovation network. Then, taking Beijing as an example, the weights and the number of epistatic relationships of each subsystem in its innovation ecosystem are calculated by BP neural network and DEMATEL method, and the NK model is modified; on this basis, the fitness values corresponding to different states of the system are calculated using MATLAB software, and the optimal evolution path of Beijing innovation ecosystem is determined through the comparison of 100,000 simulation results. The results show that the optimal evolution path of Beijing's innovation ecosystem is to create a favorable environment and culture for innovation first; then increase the input of innovation resources; and then promote the development of innovation network assets; on this basis, cultivate, attract and retain innovative talents; and finally strengthen the construction of innovation subjects.

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.

Creep Behavior Prediction Research of T92 Steel Based on BP Neural Network

2013 ◽  
Vol 467 ◽  
pp. 203-207
Author(s):  
Jian Liu
Keyword(s):  
Neural Network ◽  
Creep Behavior ◽  
Bp Neural Network ◽  
Network Theory ◽  
Experimental Results ◽  
Behavior Prediction ◽  
Forecast Precision ◽  
Network Method ◽  
Simulation Results ◽  
Neural Network Theory

Based on the BP neural network theory, the creep rate prediction model of T92 steel was established under multiple stress levels. Obtained the experimental results and using the model, the experimental results were trained. The results show that the simulation results match the measured results well with a high forecast precision. The BP neural network method can serve as research on T92 steel creep behavior.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

Character Image Recognition Based on the Improved BP Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 2655-2658 ◽  
Author(s):  
Hong Zhao
Keyword(s):  
Neural Network ◽  
Computer Simulation ◽  
Image Recognition ◽  
Bp Neural Network ◽  
Bp Algorithm ◽  
Simulation Results ◽  
Improved Bp Algorithm

This paper raises a kind of improved BP algorithm in order to compensate for some shortcomings which exist in traditional BP neural network. It has been applied to the recognition of character images. Computer simulation results demonstrate that it does bring about an ideal result.

Application of the collision mathematical model based on a BP neural network in railway vehicles

2020 ◽  
pp. 095440972095986
Author(s):  
Yu-Ru Li ◽  
Tao Zhu ◽  
Shou-Ne Xiao ◽  
Bing Yang ◽  
Guang-Wu Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Finite Element ◽  
Bp Neural Network ◽  
Learning Performance ◽  
Small Data ◽  
Data Set ◽  
Computation Efficiency ◽  
Simulation Results ◽  
Relative Errors

In order to enhance the learning performance of small-data-set models and improve the computation efficiency of finite element simulations of vehicle collision, the collision mathematical model (VCMM) based on the back-propagation (BP) neural network is established to predict the collision response data of a single car and marshalling cars at unknown velocities. The predicted results of VCMM were compared with the simulation results of the finite element method (FEM) to verify the model. The compared results show that the maximum relative errors of deformation, energy absorption and average interfacial force of a single vehicle are all below 8.5%, and the relative errors of the maximum compression of the C0 coupler and the internal energy of the A1 car among the marshalling cars are all less than 5%. In addition, the calculation time of the single car and marshalling cars collisions based on the VCMM are reduced by 24.36 and 61.8 times, respectively, compared with the FEM results, and the simulation calculation efficiency is greatly improved. The prediction result of VCMM will partially replace experimental and simulation results for crashworthiness and safety design of the vehicle structure in future studies.

Integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network

2020 ◽  
pp. 1-12
Author(s):  
Yijie Wang ◽  
Peihua Fu
Keyword(s):  
Neural Network ◽  
Supply Chain ◽  
Bp Neural Network ◽  
Strategic Alliances ◽  
Environmental Sustainability ◽  
Enterprise Management ◽  
Fuzzy Mathematics ◽  
Green Logistics ◽  
Performance Statistics ◽  
Green Suppliers

With the enhancement of people’s environmental awareness, improving environmental performance has become an important way for manufacturing enterprises to achieve sustainable development. At present, one of the key challenges facing enterprises in terms of environmental sustainability is to extend it to other supply chain members. In this paper, the author analyzes the integration performance statistics of green suppliers based on fuzzy mathematics and BP neural network. Supply chain integration represents the company’s ability to formulate strategic alliances, integrate resources, establish seamless processes and share information. The empirical results show that it is feasible to evaluate the performance of green logistics enterprise integration. Through reasonable calculation method and model, the effective and reasonable evaluation results can be obtained. Enterprise managers can reasonably evaluate the key links of enterprise management, allocate enterprise resources correctly, completely and reasonably, minimize costs and maximize profits. Therefore, for the green logistics enterprises, should pay attention to the enterprise’s own performance evaluation, timely adjust their own development direction, plan and goal.

The Design of Adaptive Noise Control System Based on Neural Network

2012 ◽  
Vol 203 ◽  
pp. 236-239
Author(s):  
Lu Jun Cui ◽  
Hui Chao Shang ◽  
Gang Zhang ◽  
Yong Li ◽  
Ze Xiang Zhao
Keyword(s):  
Neural Network ◽  
Control System ◽  
Input Signal ◽  
Bp Neural Network ◽  
Noise Control ◽  
Simulation Results ◽  
Adaptive Noise

The present work investigated the adaptive noise control system based on neural network. The structure and the characteristics of ANC algorithm were introduced in detail, at the same time, and the detailed demonstration of the BP neural network was given. Contrast verification experiments were given through the Matlab, and the simulation results have verified the effectiveness and practicability of the algorithm for the adaptive noise system in real control system. Through these methods, the disturbance of various noises in input signal could be reduced effectively.

Based on the BP Neural Network of Logistics Equipment Support Capability Evaluation

2012 ◽  
Vol 546-547 ◽  
pp. 1090-1094
Author(s):  
Jian Sheng Hao ◽  
Qi Zhi Huang ◽  
Shu Dong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Assessment Method ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Engineering Theory ◽  
Simulation Results ◽  
Capability Evaluation ◽  
Theory Research

In this paper, the system engineering theory research logistical equipment safeguard ability assessment method, and established the equipment support of the evaluation index system, using BP neural network can to approximate any nonlinear system advantage, based on the BP neural network of logistics equipment support capability evaluation model for logistics equipment safeguard the ability to provide a new method. The simulation results show that this method can ensure objectivity.

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