scholarly journals Research on the Method of Gas Emission Prediction Using Improved Grey RBF Neural Network Model

2020 ◽  
Author(s):  
Yongkang Yang ◽  
Qiaoyi Du ◽  
Chenlong Wang ◽  
Yu Bai
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Simulation Experiment ◽  
Rbf Neural Network ◽  
Grey Theory ◽  
Gas Emission ◽  
Proposed Model ◽  
Accuracy Of Prediction

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, 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 showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.

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.

Gas Emission Prediction Based on Coal Mine Operating Data

2014 ◽  
Vol 484-485 ◽  
pp. 604-607
Author(s):  
Da Chao Yuan ◽  
Xiao Guang Yue ◽  
Chen Wang ◽  
Jian Feng Zhang
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Actual Data ◽  
Data Detection ◽  
Gas Emission ◽  
Emission Data

Currently, mine accidents are recurring disasters. Among them, the coal mine gas emission data detection is important information that needed to be focus on. At first, the actual data from coal mines are gotten as a part of training and testing data. Then, BP neural network model and RBF neural network model was constructed by using Matlab. Finally, the actual data is ready for the simulation; select 15 sets of data to predict the three sets of data. BP neural network is an effective mean for gas emission, and the performance of BP neural network prediction method is better than RBF neural network method.

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 Parameter Identification of Multistage Fracturing Horizontal Well Based on PSO-RBF Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Rongwang Yin ◽  
Qingyu Li ◽  
Peichao Li ◽  
Detang Lu
Keyword(s):  
Neural Network ◽  
Parameter Identification ◽  
Network Model ◽  
Neural Network Model ◽  
Horizontal Well ◽  
Rbf Neural Network ◽  
Optimal Solution ◽  
Absolute Error ◽  
Physical Parameters ◽  
Proposed Model

In order to more accurately identify multistage fracturing horizontal well (MFHW) parameters and address the heterogeneity of reservoirs and the randomness of well-production data, a new method based on the PSO-RBF neural network model is proposed. First, the GPU parallel program is used to calculate the bottomhole pressure of a multistage fracturing horizontal well. Second, most of the above pressure data are imported into the RBF neural network model for training. In the training process, the optimization function of the global optimal solution of the PSO algorithm is employed to optimize the parameters of the RBF neural network, and eventually, the required PSO-RBF neural network model is established. Third, the resulting neural network is tested using the remaining data. Finally, a field case of a multistage fracturing horizontal well is studied by using the presented PSO-RBF neural network model. The results show that in most cases, the proposed model performs better than other models, with the highest correlation coefficient, the lowest mean, and absolute error. This proves that the PSO-RBF neural network model can be applied effectively to horizontal well parameter identification. The proposed model has great potential to improve the prediction accuracy of reservoir physical parameters.

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.

scholarly journals Neural network model for multimodal recognition of human aggression

2020 ◽  
pp. 132-149
Author(s):  
М.Ю. Уздяев
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Feature Space ◽  
Smart Environments ◽  
High Fidelity ◽  
Cyberphysical Systems ◽  
Human Aggression ◽  
Proposed Model ◽  
User Actions

Увеличение количества пользователей социокиберфизических систем, умных пространств, систем интернета вещей актуализирует проблему выявления деструктивных действий пользователей, таких как агрессия. При этом, деструктивные действия пользователей могут быть представлены в различных модальностях: двигательная активность тела, сопутствующее выражение лица, невербальное речевое поведение, вербальное речевое поведение. В статье рассматривается нейросетевая модель многомодального распознавания человеческой агрессии, основанная на построении промежуточного признакового пространства, инвариантного виду обрабатываемой модальности. Предлагаемая модель позволяет распознавать с высокой точностью агрессию в условиях отсутствия или недостатка информации какой-либо модальности. Экспериментальное исследование показало 81:8% верных распознаваний на наборе данных IEMOCAP. Также приводятся результаты экспериментов распознавания агрессии на наборе данных IEMOCAP для 15 различных сочетаний обозначенных выше модальностей. Growing user base of socio-cyberphysical systems, smart environments, IoT (Internet of Things) systems actualizes the problem of revealing of destructive user actions, such as various acts of aggression. Thereby destructive user actions can be represented in different modalities: locomotion, facial expression, associated with it, non-verbal speech behavior, verbal speech behavior. This paper considers a neural network model of multi-modal recognition of human aggression, based on the establishment of an intermediate feature space, invariant to the actual modality, being processed. The proposed model ensures high-fidelity aggression recognition in the cases when data on certain modality are scarce or lacking. Experimental research showed 81.8% correct recognition instances on the IEMOCAP dataset. Also, experimental results are given concerning aggression recognition on the IEMOCAP dataset for 15 different combinations of the modalities, outlined above.

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.

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