A Neural Network Ensemble Incorporated with Dynamic Variable Selection for Rainfall Forecast

2011 ◽  
Author(s):  
Sumi S. Monira ◽  
Zaman M. Faisal ◽  
Hideo Hirose
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Neural Network Ensemble ◽  
Dynamic Variable ◽  
Rainfall Forecast ◽  
Selection For

A Novel Quantum Neural Network Model with Variable Selection for Short Term Load Forecasting

2010 ◽  
Vol 20-23 ◽  
pp. 612-617 ◽  
Author(s):  
Wei Sun ◽  
Yu Jun He ◽  
Ming Meng
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Network Model ◽  
Neural Network Model ◽  
Load Forecasting ◽  
Short Term ◽  
Bp Network ◽  
Quantum Neural Network ◽  
Short Term Load Forecasting ◽  
Selection For

The paper presents a novel quantum neural network (QNN) model with variable selection for short term load forecasting. In the proposed QNN model, first, the combiniation of maximum conditonal entropy theory and principal component analysis method is used to select main influential factors with maximum correlation degree to power load index, thus getting effective input variables set. Then the quantum neural network forecating model is constructed. The proposed QNN forecastig model is tested for certain province load data. The experiments and the performance with QNN neural network model are given, and the results showed the method could provide a satisfactory improvement of the forecasting accuracy compared with traditional BP network model.

A Variable Selection for Asphalt Pavement Performance Based on RBF Neural Network

2015 ◽  
Vol 744-746 ◽  
pp. 1222-1225
Author(s):  
Peng Tian ◽  
Gao Feng Zhan ◽  
Lei Nai
Keyword(s):  
Neural Network ◽  
Variable Selection ◽  
Rbf Neural Network ◽  
Asphalt Pavement ◽  
Asphalt Mixture ◽  
Pavement Performance ◽  
Method Selection ◽  
Selection Of Variables ◽  
Selection For ◽  
Selection Of

By combining RBF neural network with MIV algorithm, the main influencing factors of asphalt mixture pavement performance will be selected. First, the MIV values will be calculated by MIV method. Selection of variables is based on the size of MIV. There are 8 variables selected form 12 variables. Then, a new RBF neural network will be found by the data which have great impact to the output result. The comparison between the two RBF simulate results will prove that the method of MIV is feasible in variable selection. By the MIV method, the simulate results of RBF will be calculated faster and more accurately.

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

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