scholarly journals Benefit Analysis of Low-Carbon Policy Mix Innovation Based on Consumer Perspective in Smart City

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenjie Chen ◽  
Xiaogang Wu ◽  
Ngabo Desire
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Smart City ◽  
Emission Reduction ◽  
Back Propagation ◽  
Low Carbon ◽  
Policy Mix ◽  
Trajectory Data ◽  
Carbon Policy

In the construction of smart city, the carbon emission reduction problem of road traffic needs to be solved urgently. It is of great significance to introduce reasonable low-carbon policies. Based on urban private cars trajectory data, this study, respectively, establishes the genetic algorithm-back propagation neural network model (GA-BP) and back propagation-adaptive boosting algorithm neural network model (BP-AdaBoost) to predict the carbon emissions of private cars. By comparing the two neural network models, the GA-BP neural network model has better prediction results. Next, this study establishes the cost-benefit model for consumers and compares consumers’ participation willingness, emission reduction effect, and social benefits of consumers from the perspective of six kinds of low-carbon policies. The results show that the overall effect of the low-carbon policy mix of free quota is better than that of paid quota. In addition, different low-carbon policy mixes innovations have different policy implementation effects under different indicators. Overall, the low-carbon policy mix of carbon trading and emission reduction subsidy is better in the short term, and the low-carbon policy mix of carbon tax and emission reduction subsidy is better in the long term.

scholarly journals Back-propagation neural network model to predict visibility at a road link-level

2020 ◽  
Vol 8 ◽  
pp. 100250
Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Ajinkya S. Mane ◽  
Christopher Godfrey
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Back Propagation ◽  
Back Propagation Neural Network

Application of the Back Propagation Neural Network Model on the Supplier Evaluation in E-Manufacturing Environment

2009 ◽  
Vol 16-19 ◽  
pp. 174-177
Author(s):  
Jian Chen ◽  
Ming Hong Wu ◽  
Ilias Oraifige
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation System ◽  
Supplier Selection ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Supplier Evaluation ◽  
Test Results ◽  
Model Training

The supplier evaluation is a key section of the intelligent internet supplier selection & evaluation system. The model used for supplier evaluation is Back Propagation Neural Network model which is introduced in the paper. The paper started with the brief introduction of the intelligent internet supplier selection & evaluation system. It provides a outline of the research project and then it concentrated to introduce the application of the BP NN model for supplier evaluation. The application introduced in the paper will include the design of the BP NN model, Training of the BP NN model and test results.

A NEURAL NETWORK MODEL FOR CREDIT RISK EVALUATION

2009 ◽  
Vol 19 (04) ◽  
pp. 285-294 ◽  
Author(s):  
ADNAN KHASHMAN
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Network Model ◽  
Neural Network Model ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Learning Schemes

Credit scoring is one of the key analytical techniques in credit risk evaluation which has been an active research area in financial risk management. This paper presents a credit risk evaluation system that uses a neural network model based on the back propagation learning algorithm. We train and implement the neural network to decide whether to approve or reject a credit application, using seven learning schemes and real world credit applications from the Australian credit approval datasets. A comparison of the system performance under the different learning schemes is provided, furthermore, we compare the performance of two neural networks; with one and two hidden layers following the ideal learning scheme. Experimental results suggest that neural networks can be effectively used in automatic processing of credit applications.

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