scholarly journals The Heat load Prediction Model based on BP Neural Network-markov Model

2017 ◽  
Vol 107 ◽  
pp. 296-300 ◽  
Author(s):  
Ling Xie
Keyword(s):  
Neural Network ◽  
Markov Model ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Heat Load ◽  
Load Prediction ◽  
Model Based

scholarly journals Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 16726-16741 ◽  
Author(s):  
Jiancai Song ◽  
Guixiang Xue ◽  
Xuhua Pan ◽  
Yunpeng Ma ◽  
Han Li
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Convolutional Neural Network ◽  
Heat Load ◽  
Load Prediction ◽  
Model Based

A Model Based on BP Neural Network for Audible Noise Prediction

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.

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

2019 ◽  
Vol 29 (1) ◽  
pp. 1545-1557 ◽  
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.

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

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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