Research on prediction model of asphalt pavement subsidence development based on back propagation neural network

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

Download Full-text

A novel prediction model of PM2.5 mass concentration based on back propagation neural network algorithm

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179119 ◽

2019 ◽

Vol 37 (3) ◽

pp. 3175-3183 ◽

Cited By ~ 1

Author(s):

Yegang Chen ◽

JianMei An ◽

Yanhan

Keyword(s):

Neural Network ◽

Prediction Model ◽

Mass Concentration ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Network Algorithm ◽

Neural Network Algorithm

Download Full-text

Method of Predicting Ore Dilution Based on a Neural Network and Its Application

Sustainability ◽

10.3390/su12041550 ◽

2020 ◽

Vol 12 (4) ◽

pp. 1550 ◽

Cited By ~ 3

Author(s):

Xingdong Zhao ◽

Jia’an Niu

Keyword(s):

Neural Network ◽

Prediction Model ◽

Relative Error ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Average Deviation ◽

Graph Method ◽

Neural Network Prediction ◽

Network Prediction ◽

The Difference

A back-propagation neural network prediction model with three layers and six neurons in the hidden layer is established to overcome the limitation of the equivalent linear overbreak slough (ELOS) empirical graph method in estimating unplanned ore dilution. The modified stability number, hydraulic radius, average deviation of the borehole, and powder factor are taken as input variables and the ELOS of quantified unplanned ore dilution as the output variable. The training and testing of the model are performed using 120 sets of data. The average fitting degree r2 of the prediction model is 0.9761, the average mean square error is 0.0001, and the relative error of the prediction is approximately 6.2%. A method of calculating the unplanned ore dilution is proposed and applied to a test stope of the Sandaoqiao lead–zinc mine. The calculated unplanned ore dilution is 0.717 m, and the relative error (i.e., the difference between calculation and measurement of 0.70 m) is 2.4%, which is better than the relative errors for the empirical graph method and numerical simulation (giving dilution values of 0.8 and 0.55 m, respectively). The back-propagation neural network prediction model is confirmed to predict the unplanned ore dilution in real applications.

Download Full-text