scholarly journals Study of Personal Credit Evaluation Method Based on PSO-RBF Neural Network Model

2013 ◽  
Vol 03 (04) ◽  
pp. 429-434 ◽  
Author(s):  
Shuai Li ◽  
Yuanmei Zhu ◽  
Chao Xu ◽  
Zongfang Zhou
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Credit Evaluation ◽  
Personal Credit

Assessment of the Underground Water Contaminated by the Leachate of Waste Dump of Open Pit Coal Mine Based on RBF Neural Network

2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Comprehensive Evaluation ◽  
Rbf Neural Network ◽  
Training Sample ◽  
Underground Water ◽  
Open Pit ◽  
Waste Dump

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.

scholarly journals Artificial neural network model and fuzzy logic control of dissolved oxygen in a bioreactor

2020 ◽  
Vol 17 (3) ◽  
pp. 1289
Author(s):  
Nor Hana Mamat ◽  
Samsul Bahari Mohd Noor ◽  
Laxshan A/L Ramar ◽  
Azura Che Soh ◽  
Farah Saleena Taip ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dissolved Oxygen ◽  
Network Model ◽  
Neural Network Model ◽  
Fermentation Process ◽  
Fuzzy Logic Controller ◽  
Rbf Neural Network ◽  
Aeration Rate ◽  
Process Data

In a fermentation process, dissolved oxygen is the one of the key process variables that needs to be controlled because of the effect they have on the product quality. In a penicillin production, dissolved oxygen concentration influenced biomass concentration. In this paper, multilayer perceptron neural network (MLP) and Radial Basis Function (RBF) neural network is used in modeling penicillin fermentation process. Process data from an industrial scale fed-batch bioreactor is used in developing the models with dissolved oxygen and penicillin concentration as the outputs. RBF neural network model gives better accuracy than MLP neural network. The model is further used in fuzzy logic controller design to simulate control of dissolved oxygen by manipulation of aeration rate.  Simulation result shows that the fuzzy logic controller can control the dissolved oxygen based on the given profile.

Grey RBF Neural Network Forecasting on LV under the View of Government Responsibility

2012 ◽  
Vol 452-453 ◽  
pp. 700-704
Author(s):  
Feng Rong Zhang ◽  
Annik Magerholm Fet ◽  
Xin Wei Xiao
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Development Policy ◽  
Rbf Neural Network ◽  
Good Method ◽  
Government Responsibility ◽  
Long Period ◽  
The Government

At present, the domestic research on the scale of macroscopic logistics has yet belonged to the blankness, therefore, this research tries using LV in circulation and LV in stock to measure the logistics volume and forecasting it in a long period. In order to overcome the phenomenon of “floating upward” in long-term period, this paper establish the improved Grey RBF to forecast the LV next 5-10 year in Jilin province of China. The results show that the increased circulation of goods is the main reason leading to increased logistics volume, and the simulation also shows that the improved gray RBF neural network model is a good method for the government to establish the logistics development policy.

Prediction of soybean price in China using QR-RBF neural network model

2018 ◽  
Vol 154 ◽  
pp. 10-17 ◽  
Author(s):  
Dongqing Zhang ◽  
Guangming Zang ◽  
Jing Li ◽  
Kaiping Ma ◽  
Huan Liu
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Rbf Neural Network ◽  
Soybean Price

scholarly journals Research on the Method of Methane Emission Prediction Using Improved Grey Radial Basis Function Neural Network Model

Energies ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 6112
Author(s):  
Yongkang Yang ◽  
Qiaoyi Du ◽  
Chenlong Wang ◽  
Yu Bai
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Methane Emission ◽  
Basis Function ◽  
Simulation Experiment ◽  
Rbf Neural Network ◽  
Grey Theory

Effectively avoiding methane accidents is vital to the security of manufacturing minerals. Coal mine methane accidents are often caused by a methane concentration overrun, and accurately predicting methane emission quantity in a coal mine is key to solving this problem. To maintain the concentration of methane in a secure range, grey theory and neural network model are increasingly used to critically forecasting methane emission quantity in coal mines. A limitation of the grey neural network model is that researchers have merely combined the conventional neural network and grey theory. To enhance the accuracy of prediction, a modified grey GM (1,1) and radial basis function (RBF) neural network model is proposed, which combines the amended grey GM (1,1) model and RBF neural network model. In this article, the proposed model is put into a simulation experiment, which is built based on Matlab software (MathWorks.Inc, Natick, Masezius, U.S). Ultimately, the conclusion of the simulation experiment verified that the modified grey GM (1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This shows that the modified grey GM (1,1) and RBF neural network model can make more effective and precise predict the predicts, compared to the grey GM (1,1) model and RBF neural network model.

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