Quality Prediction Model Based on PCA-BP Neural Network for Tobacco Leaves Redrying Process

Aiming to the problem that is very difficult to establish the mechanism model of quality for the process of tobacco leaves redrying, this paper proposes a quality prediction model based on principal component analysis (PCA) and improved back propagation (BP)neural network for tobacco leaves redrying process. Firstly, 12 input variables are confirmed by analyzing the factors on quality of tobacco leaves redrying process. Second, the methods of PCA is used to eliminate the correlation of original input layer data, in which 12 input variables are transformed into 6 uncorrelated indicators. Then, the quality prediction model based on improved BP neural network is established. Finally, a simulation experiment is conducted and the average prediction error is as low as 1.03%, the absolute error for forecasting is fluctuated in the range of 0.16% - 2.49%. The result indicates that the model is simpler and has higher stability for prediction, which can completely meet the actual requirements of the tobacco leaves redrying process.

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

Metals ◽

10.3390/met8080593 ◽

2018 ◽

Vol 8 (8) ◽

pp. 593 ◽

Cited By ~ 1

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.

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A Model Based on BP Neural Network for Audible Noise Prediction

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.986-987.1356 ◽

2014 ◽

Vol 986-987 ◽

pp. 1356-1359

Author(s):

You Xian Peng ◽

Bo Tang ◽

Hong Ying Cao ◽

Bin Chen ◽

Yu Li

Keyword(s):

Neural Network ◽

Prediction Model ◽

Transmission Lines ◽

Bp Neural Network ◽

Prediction Models ◽

Absolute Error ◽

Research Area ◽

Noise Prediction ◽

Bp Network ◽

Model Based

Audible noise prediction is a hot research area in power transmission engineering in recent years, especially come down to AC transmission lines. The conventional prediction models at present have got some problems such as big errors. In this paper, a prediction model is established based on BP network, in which the input variables are the four factors in the international common expression of power line audible noise and the noise value is the output. Take multiple measured power lines as an example, a train is made by the BP network and then the prediction model is set up in the hidden layer of the network. Using the trained model, the audible noise values are predicted. The final results show that the average absolute error in absolute terms of the values by the audible noise prediction model based on BP neural network is 1.6414 less than that predicted by the GE formula.

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A 4D Trajectory Prediction Model Based on the BP Neural Network

Journal of Intelligent Systems ◽

10.1515/jisys-2019-0077 ◽

2019 ◽

Vol 29 (1) ◽

pp. 1545-1557 ◽

Cited By ~ 3

Author(s):

Zhi-Jun Wu ◽

Shan Tian ◽

Lan Ma

Keyword(s):

Neural Network ◽

Prediction Model ◽

Bp Neural Network ◽

Clustering Algorithm ◽

Spline Interpolation ◽

Absolute Error ◽

Maximum Absolute Error ◽

Trajectory Prediction ◽

Model Based ◽

Crossing Point

Abstract To solve the problem that traditional trajectory prediction methods cannot meet the requirements of high-precision, multi-dimensional and real-time prediction, a 4D trajectory prediction model based on the backpropagation (BP) neural network was studied. First, the hierarchical clustering algorithm and the k-means clustering algorithm were adopted to analyze the total flight time. Then, cubic spline interpolation was used to interpolate the flight position to extract the main trajectory feature. The 4D trajectory prediction model was based on the BP neural network. It was trained by Automatic Dependent Surveillance – Broadcast trajectory from Qingdao to Beijing and used to predict the flight trajectory at future moments. In this paper, the model is evaluated by the common measurement index such as maximum absolute error, mean absolute error and root mean square error. It also gives an analysis and comparison of the predicted over-point time, the predicted over-point altitude, the actual over-point time and the actual over-point altitude. The results indicate that the predicted 4D trajectory is close to the real flight data, and the time error at the crossing point is no more than 1 min and the altitude error at the crossing point is no more than 50 m, which is of high accuracy.

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Prediction and Analysis of Strawberry Moisture Content based on BP Neural Network Model

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666190429161911 ◽

2020 ◽

Vol 13 (4) ◽

pp. 657-671

Author(s):

Wei Jiang ◽

Hongmei Xu ◽

Elnaz Akbari ◽

Jiang Wen ◽

Shuang Liu ◽

...

Keyword(s):

Neural Network ◽

Moisture Content ◽

Prediction Model ◽

Bp Neural Network ◽

Prediction Method ◽

Principal Component ◽

Spectral Difference ◽

Model Based ◽

Neural Network Prediction ◽

Network Prediction

Background: Moisture content is one of the most important indicators for the quality of fresh strawberries. Currently, several methods are usually employed to detect the moisture content in strawberry. However, these methods are relatively simple and can only be used to detect the moisture content of single samples but not batches of samples. Besides, the integrity of the samples may be destroyed. Therefore, it is important to develop a simple and efficient prediction method for strawberry moisture to facilitate the market circulation of strawberry. Objective: This study aims to establish a novel BP neural network prediction model to predict and analyze strawberry moisture. Methods: Toyonoka and Jingyao strawberries were taken as the research objects. The hyperspectral technology, spectral difference analysis, correlation coefficient method, principal component analysis and artificial neural network technology were combined to predict the moisture content of strawberry. Results: The characteristic wavelengths were highly correlated with the strawberry moisture content. The stability and prediction effect of the BP neural network prediction model based on characteristic wavelengths are superior to those of the prediction model based on principal components, and the correlation coefficients of the calibration set for Toyonaka and Jingyao respectively reached up to 0.9532 and 0.9846 with low levels of standard deviations (0.3204 and 0.3010, respectively). Conclusion: The BP neural network prediction model of strawberry moisture has certain practicability and can provide some reference for the on-line and non-destructive detection of fruits and vegetables.

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Material procedure quality forecast based on genetic BP neural network

Modern Physics Letters B ◽

10.1142/s0217984917400802 ◽

2017 ◽

Vol 31 (19-21) ◽

pp. 1740080 ◽

Cited By ~ 6

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.

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331219883501 ◽

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.

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

International Journal of Nonlinear Sciences and Numerical Simulation ◽

10.1515/ijnsns-2020-0068 ◽

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.

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