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 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 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.

An approach to carbon emissions prediction using generalized regression neural network improved by genetic algorithm

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Zhida Guo ◽  
Jingyuan Fu
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Genetic Algorithm ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Total Carbon ◽  
Generalized Regression Neural Network ◽  
Improved Method ◽  
Target Values ◽  
Generalized Regression

The study on scientific analysis and prediction of China’s future carbon emissions is conducive to balancing the relationship between economic development and carbon emissions in the new era, and actively responding to climate change policy. Through the analysis of the application of the generalized regression neural network (GRNN) in prediction, this paper improved the prediction method of GRNN. Genetic algorithm (GA) was adopted to search the optimal smooth factor as the only factor of GRNN, which was then used for prediction in GRNN. During the prediction of carbon dioxide emissions using the improved method, the increments of data were taken into account. The target values were obtained after the calculation of the predicted results. Finally, compared with the results of GRNN, the improved method realized higher prediction accuracy. It thus offers a new way of predicting total carbon dioxide emissions, and the prediction results can provide macroscopic guidance and decision-making reference for China’s environmental protection and trading of carbon emissions.

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).

An Improved Prediction Method of SOC Based on the GA-RBF Neural Network

2014 ◽  
Vol 953-954 ◽  
pp. 800-805 ◽  
Author(s):  
Meng Di Liang ◽  
Tie Zhou Wu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Prediction Method ◽  
Charged State ◽  
Improved Genetic Algorithm ◽  
State Prediction ◽  
Radial Basis

Concerning the prediction problems’accuracy of the state-of-charge(SOC) of the battery，this paper proposes a prediction method based on an improved genetic algorithm-radial basis function neural network for power battery charged state. The prediction method, based on intensity of information interaction and neural activity, adjusts the size of the neural network online and solves the problem that radial basis function neural network structure adjustment influences the accuracy of charged state prediction. The simulation results show that，compared with the method of radial basis function neural network based on genetic algorithm , the accuracy of charged state prediction is more stable and more precise.

Modeling a Delayed Coking Process with GRNN and Double-Chain Based DNA Genetic Algorithm

2010 ◽  
Vol 8 (1) ◽  
Author(s):  
Xiao Chen ◽  
Ning Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Cross Validation ◽  
Training Data ◽  
Generalized Regression Neural Network ◽  
Genetic Operators ◽  
Double Chain ◽  
Computer Prediction ◽  
Delayed Coking ◽  
Smoothing Parameters

For characterization or optimization process, a computer prediction model is in demand. This paper describes an approach for modeling a delayed coking process using generalized regression neural network (GRNN) and a double-chain based DNA genetic algorithm (dc-DNAGA). In GRNN, the smoothing parameters have significant effect on the performance of the network. This paper presents an improved GA, dc-DNAGA, to optimize the smoothing parameters in GRNN. The dc-DNAGA is inspired by the biological DNA, where the smoothing parameters are coded in the double-chain chromosomes and modified genetic operators are employed to improve the global search ability of GA. To test the performance of the constructed model, it is used to predict the output of the test data which is not included in the training data. Compared with other reported methods, eight cross validation results show the advantage of the proposed technique that it predicts the new data more accurately.

Generalized Regression Neural Network (GRNN) for the Prediction of CRDI Engine Responses Fuelled with Pongamia Biodiesel

2021 ◽  
Vol 08 (01) ◽  
pp. 5-12
Author(s):  
Nithyananda BS ◽  
Keyword(s):  
Neural Network ◽  
Direct Injection ◽  
Injection Pressure ◽  
Cost Effective ◽  
General Regression Neural Network ◽  
Generalized Regression Neural Network ◽  
Injection Timing ◽  
Performance Parameters ◽  
Performance And Emission ◽  
Testing Dataset

The application of General Regression Neural Network (GRNN) for the prediction of performance and emission responses of Common Rail Direct Injection (CRDI) engine using B5, B10 and B20 blend of pongamia biodiesel is presented in this paper. Data required for the prediction is obtained through experimentation on CRDI engine by varying parameters like injection pressure, injection timing and fuel preheating temperature. The experiments were conducted based on L9 Taguchi Orthogonal Array (OA). The experimental values for performance parameters like brake thermal efficiency, specific fuel consumption and emission parameters like CO, Nox and HC were recorded and used for GRNN. GRNN model is trained with 70% of samples and is validated with testing dataset of 30% by selecting optimum spread parameter (σ). The proposed model was found to be reliable and provides a cost effective way for determining performance parameters of engine. The results presented in this study substantially promote the use of GRNN model for the prediction of parameters in CRDI engine.

The Distribution of Teaching Staff in Colleges and Universities based on Neuropsychology

2021 ◽  
Vol 7 (5) ◽  
pp. 4682-4692
Author(s):  
Ruolin Yang ◽  
Dan Guo
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Colleges And Universities ◽  
Quality Education ◽  
Generalized Regression Neural Network ◽  
Teaching Staff ◽  
Actual Situation ◽  
Teacher Resources ◽  
Calculation Process ◽  
Generalized Regression

Objectives: At present, quality education has gradually been recognized by the whole society, and a consensus has been reached on its importance, which has put forward stricter requirements for the distribution of faculty in universities. Methods: In this paper, based on neuropsychology, the distribution of teaching staff in colleges and universities was studied, and the model of talent evaluation and distribution was constructed. Results: Firstly, the generalized regression neural network was optimized by genetic algorithm. Then, the genetic algorithm’s generalized regression neural network calculation process was designed. Conclusion: Finally, with the example of teacher resources in a university, the algorithm in this paper was tested. The results show that the results of the generalized regression neural network optimized by genetic algorithm can match the actual situation very well, and the method is feasible with certain advantages.

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