scholarly journals Calculation for Primary Combustion Characteristics of Boron-Based Fuel-Rich Propellant Based on BP Neural Network

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Wu Wan'e ◽  
Zhu Zuoming
Keyword(s):  
Neural Network ◽  
Burning Rate ◽  
Ammonium Perchlorate ◽  
Bp Neural Network ◽  
Calculation Model ◽  
Combustion Characteristics ◽  
Experimental Results ◽  
Calculation Error ◽  
Backpropagation Neural Network ◽  
Practical Scheme

A practical scheme for selecting characterization parameters of boron-based fuel-rich propellant formulation was put forward; a calculation model for primary combustion characteristics of boron-based fuel-rich propellant based on backpropagation neural network was established, validated, and then was used to predict primary combustion characteristics of boron-based fuel-rich propellant. The results show that the calculation error of burning rate is less than %; in the formulation range (hydroxyl-terminated polybutadiene 28%–32%, ammonium perchlorate 30%–35%, magnalium alloy 4%–8%, catocene 0%–5%, and boron 30%), the variation of the calculation data is consistent with the experimental results.

scholarly journals Insulation Monitoring of Dynamic Wireless Charging Network Based on BP Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 129
Author(s):  
Feng Wen ◽  
Wenjie Pei ◽  
Qiang Li ◽  
Zhoujian Chu ◽  
Wenhan Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Power Conversion ◽  
Experimental Results ◽  
Calculation Error ◽  
Insulation Resistance ◽  
Wireless Charging ◽  
Monitoring Method ◽  
Insulation Monitoring ◽  
On Line

The transmission cable and power conversion device need to be buried underground for dynamic wireless charging of an expressway, so cable insulation deterioration caused by aging and corrosion may occur. This paper presents an on-line insulation monitoring method based on BP neural network for dynamic wireless charging network. The sampling signal expression of the injection signal is derived, and the feasibility of this method is verified by experiments, which effectively overcomes the problem of large calculation error of insulation resistance when the cable capacitance to ground is large. The experimental results indicate that the error of the proposed method is less than 9%, which can meet the needs of insulation monitoring.

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.

Study on Method of Recognizing Characteristics of Pipeline Leakage Acoustic Signals

2006 ◽  
Author(s):  
Likun Wang ◽  
Dongjie Tan ◽  
Yongjun Cai ◽  
SongGuang Fu ◽  
Jian Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Field Experiment ◽  
Frequency Band ◽  
Bp Neural Network ◽  
Acoustic Signals ◽  
Pressure Variation ◽  
Experimental Results ◽  
Acoustic Sensors ◽  
Experiment Data

Wavelet package and neural network are used to recognize the characteristics of pipeline leakage acoustic signals. Acoustic signals produced by pressure variation of pipelines can be detected by the acoustic sensors installed on the pipelines. The detecting accuracy can be increased with recognizing the acoustic signals correctly. The method to detect acoustic signals by combining the wavelet package and neural network is introduced in this paper. The signal is decomposed with wavelet package firstly, then the decomposed coefficients in each frequency band are obtained through reconstruction. As a result, the parameters of the new sequences reconstructed on every decomposed node are acquired, and then these parameters are input to BP neural network to recognize the fault reason intelligently. At the end of the paper, field experiment data and their analyzed results are studied. The experimental results are provided to show that the proposed method can increase the accuracy efficiently.

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.

Optimization of Combustion Characteristics of Mg/PTFE Fuel-Rich Pyrotechnical Propellant Based on Neural Network Tool Kit of MATLAB

2013 ◽  
Vol 389 ◽  
pp. 924-929
Author(s):  
Lei Fan ◽  
Gong Pei Pan ◽  
Di Hua Ouyang ◽  
Xin Chen ◽  
Bin Guo
Keyword(s):  
Neural Network ◽  
Programming Language ◽  
Bp Neural Network ◽  
Combustion Characteristics ◽  
Combustion Characteristic ◽  
Huge Amount ◽  
Matlab Programming

Configuration and algorithm of BP neural network and characteristics of MATLAB programming language are introduced in this article. Establishing BP neural network becomes easy with relevant functions of design, training and simulation of neural network provided by neural network tool kit of MATLAB. Users can call relevant programs according to their own requirements without being bothered about writing huge amount of algorithm programs. This paper discusses the methods and procedure of designing BP neural network with MATLAB neural network tool kit in detail. Optimization of combustion characteristic of Mg/PTFE fuel-rich pyrotechnical propellant was taken as an example to verify the feasibility of the approach.

Fault Diagnosis for Electrical Control System of Automobile Based on Support Vector Machine

2014 ◽  
Vol 666 ◽  
pp. 203-207
Author(s):  
Jian Hua Cao
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Control System ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Experimental Results ◽  
Support Vector ◽  
Electrical Control ◽  
Diagnosis Method ◽  
The Common

This paper is to present a fault diagnosis method for electrical control system of automobile based on support vector machine. We collect the common fault states of electrical control system of automobile to analyze the fault diagnosis ability of electrical control system of automobile based on support vector machine. It can be seen that the accuracy of fault diagnosis for electrical control system of automobile by support vector machine is 92.31%; and the accuracy of fault diagnosis for electrical control system of automobile by BP neural network is 80.77%. The experimental results show that the accuracy of fault diagnosis for electrical control system of automobile of support vector machine is higher than that of BP neural network.

scholarly journals Packet Loss Differentiation Over Manet Based on a BP Neural Network

2018 ◽  
Vol 15 (1) ◽  
Author(s):  
Dimitris Kanellopoulos ◽  
Pratik Gite
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Packet Loss ◽  
Ad Hoc ◽  
Routing Algorithm ◽  
Backpropagation Neural Network ◽  
Packet Losses ◽  
Simulation Experiments ◽  
Data Packets ◽  
Simulation Results

An adaptive distributed routing algorithm is essential in MANETs, since there is no central routing system. Actually, there is no central point of coordination; each node is responsible for forwarding data packets to other nodes, thereby acting as router and host. A packet might travel through multiple intermediary ad hoc nodes in order to arrive to its destination, while the nature of wireless multi-hop channel is bringing in various types of packet losses. This paper focuses on three main reasons of online packet losses in MANETs: (1) losses due to wireless link errors; (2) losses due to congestion; and (3) losses due to route alteration. It proposes a deep learning-based algorithm for packet loss discrimination. The algorithm uses the backpropagation neural network (BPNN) concept. We performed simulation experiments for evaluating the performance of the proposed loss discrimination algorithm under different network configurations. Through simulation results, we confirmed that the proposed algorithm improves packet loss discrimination and route alteration in the network. It also reduces congestion and increases network throughput.

The Application of Modified BP Neural Network Method to the Evaluation of the Quality of Teaching

2013 ◽  
Vol 710 ◽  
pp. 628-632
Author(s):  
Rong Song ◽  
Tao Liu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Calculation Error ◽  
Neural Network Method ◽  
Network Method ◽  
Calculation Results ◽  
Function Relationship ◽  
The Stability

Modified BP neural network method was used to solve the problem of teaching quality evaluation. The neural network was built to fit the function relationship between the second-floor indicator and teaching quality evaluation. So quality teaching evaluation could be implemented. At first, the theory of BP neural network method was introduced, then, students` evaluation of the secondary indicators was taken as inputs, and scores from the Steering Group as output, and 20 lessons scores as researched data, and then, calculating characters of BP method were analyzed. The calculating result showed that the calculation results of the method have the stability, its feasibility was proved. After that, the optimized calculating method was used to optimize result. The calculation results showed that the method had high accuracy, and predictive value calculation error was less than 2.02%, and it verified the feasibility of the method.

scholarly journals Analysis of DES Plaintext Recovery Based on BP Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-5
Author(s):  
Sijie Fan ◽  
Yaqun Zhao
Keyword(s):  
Neural Network ◽  
Error Rate ◽  
Bp Neural Network ◽  
Layer By Layer ◽  
Network Simulator ◽  
Analysis Model ◽  
Backpropagation Neural Network ◽  
Network Algorithms ◽  
The Neural Network ◽  
Neural Network Algorithm

Backpropagation neural network algorithms are one of the most widely used algorithms in the current neural network algorithm. It uses the output error rate to estimate the error rate of the direct front layer of the output layer, so that we can get the error rate of each layer through the layer-by-layer backpropagation. The purpose of this paper is to simulate the decryption process of DES with backpropagation algorithm. By inputting a large number of plaintext and ciphertext pairs, a neural network simulator for the decryption of the target cipher is constructed, and the ciphertext given is decrypted. In this paper, how to modify the backpropagation neural network classifier and apply it to the process of building the regression analysis model is introduced in detail. The experimental results show that the final result of restoring plaintext of the neural network model built in this paper is ideal, and the fitting rate is higher than 90% compared with the true plaintext.

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