Investigation on the ignition delay prediction model of multi-component surrogates based on back propagation (BP) neural network

2022 ◽  
Vol 237 ◽  
pp. 111852
Author(s):  
Yanqing Cui ◽  
Haifeng Liu ◽  
Qianlong Wang ◽  
Zunqing Zheng ◽  
Hu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ignition Delay ◽  
Back Propagation ◽  
Delay Prediction

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Weight Loss Prediction Model for Pig Carcass Based on a Genetic Algorithm Back- Propagation Neural Network

2020 ◽  
Vol 63 (4) ◽  
pp. 1071-1077
Author(s):  
Chenyang Sun ◽  
Lusheng Chen ◽  
Yinian Li ◽  
Hao Yao ◽  
Nan Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Weight Loss ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Time Interval ◽  
Spraying Parameters ◽  
Pig Carcasses

HighlightsWe propose five spraying parameters according to the characteristics of pig carcasses in the spray-chilling process.A prediction model for pig carcass weight loss, based on a genetic algorithm back-propagation neural network, is proposed to reveal the relationship between weight loss and spraying parameters.To study the effects of various spraying parameters on weight loss, an automatic spray-chilling device was designed, which can modify up to five spraying parameters.Abstract. Because the weight loss of a pig carcass in the spray-chilling process is easily affected by the spraying frequency and duration, a prediction model for weight loss based on a genetic algorithm (GA) back-propagation (BP) neural network is proposed in this article. With three-way crossbred pig carcasses selected as the test materials, the duration and time interval of high-frequency spraying, the duration and time interval of low-frequency spraying, and the duration of a single spray were selected as inputs to the network model. The weight and threshold of the network were then optimized by the GA. The prediction model for pig carcass weight loss established by the GA BP neural network yielded a correlation coefficient of R = 0.99747 between the network output value of the test samples and the target value. Weight loss prediction by the model is feasible and allows better expression of the nonlinear relationship between weight loss and the main controlling factors. The results can be a reference for chilled meat production. Keywords: BP neural network, Genetic algorithm, Pig carcass, Predictive model, Weight loss

Predicting delays in prefabricated projects: SD-BP neural network to define effects of risk disruption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ying Zhao ◽  
Wei Chen ◽  
Mehrdad Arashpour ◽  
Zhuzhang Yang ◽  
Chengxin Shao ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Input Parameter ◽  
Model System ◽  
Extreme Condition ◽  
Content Type ◽  
Schedule Delay ◽  
Wide Range ◽  
Delay Prediction

PurposePrefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.Design/methodology/approachThis paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.FindingsThe established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.Originality/valueThis paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

Material procedure quality forecast based on genetic BP neural network

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740080 ◽  
Author(s):  
Bao-Hua Zheng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
High Accuracy ◽  
Process Quality ◽  
Quality Prediction ◽  
Fast Convergence Rate ◽  
Material Process ◽  
Quality Forecast

Material procedure quality forecast plays an important role in quality control. This paper proposes a prediction model based on genetic algorithm (GA) and back propagation (BP) neural network. It can obtain the initial weights and thresholds of optimized BP neural network with the GA global search ability. A material process quality prediction model with the optimized BP neural network is adopted to predict the error of future process to measure the accuracy of process quality. The results show that the proposed method has the advantages of high accuracy and fast convergence rate compared with BP neural network.

Prediction of Cutting Tool Life Based on ACO-BP Meural Network

2013 ◽  
Vol 655-657 ◽  
pp. 1714-1717 ◽  
Author(s):  
Tie Liu Wang ◽  
Xian Ming Chen ◽  
Shui Bin Chen
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Tool Life ◽  
Bp Neural Network ◽  
Cutting Tool ◽  
Back Propagation ◽  
Global Search ◽  
Machining Parameters ◽  
The Cost ◽  
Search Capability

For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.

Deformation Monitoring Model of Jinping-I Hydropower Station Based on NACA-BP Neural Network

2014 ◽  
Vol 704 ◽  
pp. 257-260
Author(s):  
De Wen Cai ◽  
Chen Fei Shao ◽  
Di Kai Wang ◽  
Er Feng Zhao ◽  
Meng Yang
Keyword(s):  
Neural Network ◽  
Local Search ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Deformation Monitoring ◽  
Ant Colony ◽  
Monitoring Data ◽  
Neural Network Modeling

Back Propagation (BP) neural network can learn and store a large number of input-output model nonlinear relationships with simple structure. Niche ant colony algorithm (NACA) combines the ant colony algorithm (ACA) with the niche technology in order to add its local search ability to ACA with preserving the intelligent search ability and robustness of ACA. To optimize predicting model establishment of the dam monitoring data, NACA and BP neural network modeling method are combined to establish a prediction model of horizontal displacement monitoring data. The traditional BP neural network prediction model is established to make a comparison with the NACA. The results show that NACA-BP neural network method can speed up the convergence rate of BP neural network and enhance local search ability and prediction accuracy.

Application of artificial neural networks for prediction of sinter quality based on process parameters control

2019 ◽  
Vol 42 (3) ◽  
pp. 422-429
Author(s):  
Huijun Shao ◽  
Zhengming Yi ◽  
Zhuo Chen ◽  
Zheng Zhou ◽  
Zhidan Deng
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Process Parameters ◽  
Bp Neural Network ◽  
Control Point ◽  
Back Propagation ◽  
Learning Ability ◽  
Quality Prediction ◽  
Sintering Process ◽  
Evaluation Indexes

According to the characteristics of non-linearity, strong coupling and a large time delay in the sintering process, the overall analysis for the sintering process has been carried out from the process parameter control point. The sinter performance evaluation indexes and the main influential parameters were determined. The quality prediction model for the sintering process was established using back propagation (BP) neural network algorithm with momentum and variable learning rate. The simulation experimental results show that the model has a higher prediction accuracy and a stronger self-learning ability. The predictive hit rate of random samples is over 81% by adopting BP neural network with the structure of 15-24-4 and network error is 0.65×10−3, thereby verifying the accuracy and effectiveness of the quality prediction model on the basis of process parameters control.

scholarly journals Research on prediction model of thermal and moisture comfort of underwear based on principal component analysis and Genetic Algorithm–Back Propagation neural network

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Pengpeng Cheng ◽  
Daoling Chen ◽  
Jianping Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Principal Component Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Back Propagation ◽  
Principal Component ◽  
Component Analysis ◽  
Back Propagation Neural Network

AbstractIn order to improve the efficiency and accuracy of thermal and moisture comfort prediction of underwear, a new prediction model is designed by using principal component analysis method to reduce the dimension of related variables and eliminate the multi-collinearity relationship between variables, and then inputting the converted variables into genetic algorithm (GA) and BP neural network. In order to avoid the problems of slow convergence speed and easy falling into local minimum of Back Propagation (BP) neural network, this paper adopted GA to optimize the weights and thresholds of BP neural network, and utilized MATLAB software to program, and established the prediction models of BP neural network and GA–BP neural network. To verify the superiority of the model, the predicted result of GA–BP, PCA–BP and BP are compared with GA–BP neural network. The results show that PCA could improve the accuracy and adaptability of GA–BP neural network for thermal and moisture comfort prediction. PCA–GA–BP model is obviously superior to GA–BP, PCA–BP, BP, SVM and K-means prediction models, which could accurately predict thermal and moisture comfort of underwear. The model has better accuracy prediction and simpler structure.

scholarly journals A Study on a Prediction Model of E-Bike Expansion Degree at Irregular Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6852
Author(s):  
Ting Tan ◽  
Jianxiao Ma ◽  
Zhen Yang ◽  
Mengyue Zhu ◽  
Chenhong Zong ◽  
...  
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Green Light ◽  
Signalized Intersections ◽  
Motor Vehicles ◽  
Algorithm Optimization ◽  
Extraction Technology ◽  
Main Research

The deviations of straight-going traffic at irregular signalized intersections lead to obvious expansion characteristics of e-bikes. This situation increases the possibility of collisions between motor vehicles and e-bikes. In order to study the change of expansion degree of straight-going e-bike at irregular signalized intersections, the video trajectory extraction technology is used to obtain relevant data of e-bikes during green light release periods at irregular signalized intersections. In addition, we combined the flow and spacing characteristics of e-bikes and used a clustering method to analyze the release stage and release groups. Therefore, the Group 1 of e-bikes in the early green light release was determined to be the main research object of expansion degree. According to the static and dynamic factors, a prediction model for the expansion degree of straight-going e-bikes at irregular signalized intersections was established based on the beetle antennae search–back propagation (BAS-BP) neural network model. Finally, the evaluation indexes were compared and analyzed before and after the beetle antennae search (BAS) algorithm optimization. The results showed that the BAS-BP neural network prediction model was better than that of the back propagation (BP) neural network. The results could provide a theoretical reference for improving the efficiency of mixed traffic flow at irregular signalized intersections.

scholarly journals Research on Prediction of Rating of Rockburst Based on BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 463-469 ◽  
Author(s):  
Xiaobo Xiong
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Rock Burst ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Good Prospect ◽  
Underground Engineering ◽  
Minimum Stress ◽  
Set Up ◽  
Economic Construction

With the development of economic construction, underground space development continues to move towards the deep. "More, long, big, deep," will be the general trend of the development of underground engineering in the 21st century. Rock burst is a kind of sudden geological disasters with a higher frequency in deep tunnel construction. Rock burst prediction has very important significance for the construction of underground engineering in highland stress area. This paper described the mechanism of rockburst. The researchers systematically analyzed relevant factors of rockburst. In this paper, the principle and application of Back-Propagation (BP) neural network were introduced, and to improve the algorithm of neural network, the NNT prediction model was set up. The author have taken the seven parameters including (as input values): Index of brittleness, Ratio of Strength stress, Ratio of maximum stress to minimum stress, Depth of engineering, Completeness of rockmass, Structural strength, Depth of pit for rock burst. The results of rockburst also proved the prediction model has high accuracy and stability, indicating that the model has a good prospect in the rock burst forecasting.

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