Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

2018 ◽  
Vol 27 (2) ◽  
pp. 291-302 ◽  
Author(s):  
Zhi-da Guo ◽  
Jing-Yuan Fu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Railway Freight ◽  
Accurate Forecast ◽  
Freight Volume ◽  
Construction Operation ◽  
General Regression

AbstractRailway freight transportation is an important part of the national economy. Accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision making of railways. After analyzing the application status of generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model by using improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of GRNN, and then the optimal spread is used for forecasting in GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared with the results of GRNN, a higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method.

Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network

2017 ◽  
Vol 28 (5) ◽  
pp. 835-848
Author(s):  
Zhi-da Guo ◽  
Jing-Yuan Fu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Supply And Demand ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Railway Freight ◽  
Freight Volume ◽  
Construction Operation

Abstract Railway freight transportation is an important part of the national economy. The accurate forecast of railway freight volume is significant to the planning, construction, operation, and decision-making of railways. Railway freight volume forecasting methods are complex and nonlinear due to the imbalance of supply and demand in the railway freight market as well as the complicated and different influences of various factors on freight volume. The relation between some information is easily ignored when the traditional method of railway freight volume forecasting is used for prediction based on causality or time series. After analyzing the application status of the generalized regression neural network (GRNN) in the prediction method of railway freight volume, this paper improves the performance of this model using an improved neural network. In the improved method, genetic algorithm (GA) is adopted to search the optimal spread, which is the only factor of the GRNN, and then the optimal spread is used for forecasting in the GRNN. In the process of railway freight volume forecasting, through this method, the increments of data are taken in the calculation process and the goal values are obtained after calculation as the forecasted results. Compared to the results of the GRNN, higher prediction accuracy is obtained through the GA-improved GRNN. Finally, the railway freight volumes in the next 2 years are forecasted based on this method and this improved method can provide a new approach for predicting the railway freight volume.

Prediction Models for Railway Freight Volume Based on Artificial Neural Networks

2014 ◽  
Vol 543-547 ◽  
pp. 2093-2098 ◽  
Author(s):  
Yan Sun ◽  
Mao Xiang Lang ◽  
Dan Zhu Wang ◽  
Lin Yun Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Prediction Accuracy ◽  
Freight Transport ◽  
Generalized Regression Neural Network ◽  
Railway Freight ◽  
Freight Volume ◽  
Artificial Neural ◽  
Generalized Regression

The current China railway freight transport has always been faced with the situation of limited transport resources. Many relative studies have been done to solve the problem of resource shortage. And railway freight volume prediction is the basis of all these studies. With accurate volume prediction, railway freight transport administrations can precisely allocate the transport resources, such as wagons and locomotives. In order to overcome the limitations of traditional prediction methods, in this study, we design four artificial neural network models for prediction, including BP neural network model, linear neural network model, RBF neural network model and generalized regression neural network model. The results of simulation and comparison show that all these models can reach high prediction accuracy and generalized regression neural network has both higher prediction accuracy and better curve fitting capacity compared with other models.

Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network

2014 ◽  
Vol 15 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Zhuomin Wang ◽  
Dongguo Shao ◽  
Haidong Yang ◽  
Shuang Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Water Transfer ◽  
General Regression Neural Network ◽  
Quality Data ◽  
Water Delivery ◽  
The South ◽  
North Water ◽  
General Regression

The safety of water delivery and water quality in the South to North Water Transfer Project of China is important to northern China. Water quality data, flow data and data on factors that influence water quality were collected from 25 May to 26 August, 2013. These data were used to forecast water quality and calculate the relative error when using a genetic algorithm optimized general regression neural network (GA-GRNN) model as well as conventional general regression neural network (GRNN) and genetic algorithm optimized back propagation (GA-BP) models. The GA-GRNN method requires few network parameters and has good network stability, a high learning speed and strong approximation ability. The overall forecasted result of GA-GRNN is the best of three models, of which the root mean square error (RMSE) of every index is nearly the least among three models. The results reveal that the GA-GRNN model is efficient for water quality prediction under normal conditions and it can be used to ensure the security of water delivery and water quality in the South to North Water Transfer Project.

scholarly journals Labeling Confidence Values for Wafer-Handling Robot Arm Performance Using a Feature-Based General Regression Neural Network and Genetic Algorithm

2019 ◽  
Vol 9 (20) ◽  
pp. 4241
Author(s):  
Yi-Cheng Huang ◽  
Zi-Sheng Yang ◽  
Hsien-Shu Liao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Feature Extraction ◽  
Ccd Camera ◽  
Remaining Useful Life ◽  
General Regression Neural Network ◽  
Robot Arm ◽  
Feature Extraction Method ◽  
A Charge ◽  
General Regression

The prognosis and management of machine health statuses are emerging research topics. In this study, the performance degradation of a wafer-handling robot arm (WHRA) was predicted using the proposed machine-learning approach. This method considers the eccentric vertical and planar position deviations from a wafer mark using a charge-coupled device (CCD) camera. Synthesized position signals were defined using the square root of x- and y-axes deviations in the horizontal view and the square of the wafer mark diameter in the vertical view. A feature extraction method was used to determine the position status on the basis of these displacements and the area of a wafer mark in a CCD image. The root mean square error and mean, maximum, and minimum of the synthesized position signals were extracted through feature extraction and used for data mining by a general regression neural network (GRNN) and logistic regression (LR) models. The lifetime assessment by confidence value of the WHRA’s remaining useful life (RUL) by the genetic algorithm/GRNN exhibited nearly the same trend as that predicted through a run-to-failure LR model. The experimental results indicated that the proposed methodology can be used for proactive assessments of the RUL of WHRAs.

scholarly journals Wind power short-term power forecasting based on Improved Grey Wolf algorithm and Optimized Generalized Regression Neural Network

2021 ◽  
Vol 2085 (1) ◽  
pp. 012020
Author(s):  
Yiwen Hu ◽  
Yang Gao ◽  
Shuai Yang
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Wind Power ◽  
Prediction Method ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Gray Wolf ◽  
Power Prediction ◽  
Bp Network ◽  
Generalized Regression

Abstract Aiming at the problem of wind turbine output prediction, a wind power prediction method based on Improved Gray Wolf algorithm and optimized generalized regression neural network is proposed in this paper. Firstly, according to the daily similarity of wind speed and wind power, cluster analysis is used to classify the data. Considering that the degree of each factor affecting wind power output changes, based on the selection of similar days, an improved gray wolf algorithm is introduced to optimize the weight of each influencing factor. The two models of the first mock exam are selected to input the radial single mode function RBF and the back propagation (BP) network to predict the output of the wind turbine separately. The prediction results of the two models are input to the generalized regression neural network optimized by the Wolf Wolf algorithm and the nonlinear combination forecasting is carried out. The basis models are used to predict the output of the wind turbine. The example analysis of an area shows that the model can be closer to the real value in the peak and valley of the prediction curve and has higher prediction accuracy than the combined prediction model of single BP, RBF and non optimized general regression neural network (GRNN).

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

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