Efficiency Evaluation Method of Simulation System Based on BP Neural Network

2011 ◽  
Author(s):  
Chu Wei
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Simulation System ◽  
Efficiency Evaluation

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

A New Method to Predict Gas Production Based on Fuzzy BP Artificial Neural Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 688-691
Author(s):  
Ran Zhang ◽  
Jun Zhou ◽  
Cheng Yong Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Gas Production ◽  
Gas Well ◽  
Well Productivity ◽  
Qualitative Factors ◽  
Productivity Prediction

BP neural network has been successfully used in the gas well productivity prediction, but as a result of neural network is sensitive to the number of input parameters, we had to ignore some factors that is less important to the gas well productivity. In addition, the existing various productivity prediction method cannot consider the influence of some important qualitative factors. This article integrated the advantages of fuzzy comprehensive evaluation and BP neural network, fuzzy comprehensive evaluation method is used to construct the BP neural network's input matrix, and BP neural network learning function is used to solve the connection weights, so as to achieve the aim of predicting gas production. This method not only can consider as many factors influence on gas well production, ut also can consider qualitative factors, so the forecast results of the new model are more realistically close to the actual production situation of reservoirs.

Study on the Evaluation of Scientific Research Achievements Based on the BP Neural Network

2012 ◽  
Vol 170-173 ◽  
pp. 3436-3439
Author(s):  
Xiao Hui Hou ◽  
Lei Huang ◽  
Xue Fei Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Index System ◽  
Evaluation Method ◽  
Scientific Research ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Evaluation Result ◽  
Set Up ◽  
Solution Efficiency

The scientific research achievements are evaluated based on the BP neural network method which is developed in this paper. According to the analysis and consult with the well-known experts, set up the evaluation index system of scientific research achievements, and based on it, the BP neural network model which is used to evaluate the scientific research achievements is established. Through an actual example, in order to improve the solution efficiency, use the Matlab software to solve the model and get the evaluation result of the scientific research achievements in the example. The evaluation result has high accuracy and could meet the basic actual needs. The evaluation method which is set up in this paper will benefit to our country's evaluation index system of the scientific research achievements and will promote the development of evaluation methods of the scientific research achievements.

Safety Assessment of Transmission Grid Based on Fuzzy Comprehensive Evaluation and BP Neural Network

2010 ◽  
Vol 439-440 ◽  
pp. 528-533
Author(s):  
Yuan Sheng Huang ◽  
Wei Fang ◽  
Cheng Fang Tian
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Safety Assessment ◽  
Evaluation Method ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Transmission Grid ◽  
Comprehensive Evaluation Method ◽  
System Method ◽  
Grid Based

In the practice of safety assessment on transmission grid, there is the variation degree of many indexes which can not be accurately described, and fuzzy comprehensive evaluation method can reflect the safety degree of every element. In addition, the combination use of BP neural network and expert system method can determine impact extent of assessment factors on safety of transmission grid and the weight of each factor relative to safety of transmission grid. Therefore, the paper proposes the safety assessment of transmission grid based on BP neural network and fuzzy comprehensive evaluation. Finally, an example is used to prove the method is high precision and practical.

Research on configuration design and operation effect evaluation for ultra high voltage (UHV) vertical insulator cleaning robot

2019 ◽  
Vol 47 (1) ◽  
pp. 90-101 ◽  
Author(s):  
Yu Yan ◽  
Wei Jiang ◽  
An Zhang ◽  
Qiao Min Li ◽  
Hong Jun Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Motion Planning ◽  
High Voltage ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Effect Evaluation ◽  
Content Type ◽  
Network Training ◽  
Cleaning Robot ◽  
Physical Prototype

Purpose This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning. Design/methodology/approach The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed. Findings Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization. Originality/value An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

The Intelligence Evaluation Method Based on BP Neural Network

2013 ◽  
Vol 8 (2) ◽  
pp. 438-444 ◽  
Author(s):  
Yang LIU ◽  
QinLiang LI ◽  
HongLiang YAO ◽  
BangChun WEN
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Evaluation Method
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