Prediction Model of MBR Membrane Flux for Elman Neural Network Based on PSO-GA Hybrid Algorithm

Purpose This study aims to establish a dynamic process model of an electromagnetic thermotherapy system (ETS) to predict the temperature of a thermotherapy needle. Design/methodology/approach The model is used for real-time predicting the static and dynamic responses of temperature and can therefore provide a valuable analysis for system monitoring. Findings The electromagnetic thermotherapy process is a nonlinear problem in which the system identification is implemented by a neural network identifier. It can simulate the input/output relationship of a real system with an excellent approximation ability to uncertain nonlinear system. A system identifier for an ETS is analyzed and selected with recurrent neural networks models to deal with various treatment processes. Originality/value The Elman neural network (ENN) prediction model on ETS proposed in this study is an easy and feasible method. Comparing two situations of inputs with more and fewer data, both are trained to present low mean squared error, and the temperature response error appears within 15 per cent. The ENN, with the advantages of simple design and stable efficacy, is useful for establishing the temperature prediction model to ensure the security in the thermotherapy.

Download Full-text

Application of Gray Elman Neural Network to Predict the Gas Emission Amount

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.706-708.1750 ◽

2013 ◽

Vol 706-708 ◽

pp. 1750-1754 ◽

Cited By ~ 1

Author(s):

Jing Gang Zhang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Control Measures ◽

Grey Theory ◽

Grey Prediction ◽

Gas Emission ◽

Elman Neural Network ◽

Artificial Neural ◽

Mine Gas

The prediction of mine Gas Emission Amount is an important part of helping to make rational gas control measures. In order to improve the accuracy of mine gas emission prediction, this paper introduced the grey theory into the Elman artificial neural network theory, and combined the gray prediction model GM (1,1) with the Elman neural network model,established a gray Elman artificial neural network prediction model of gas emission, and carried on the simulation through software Matlab. Practice and experiment showed that this method compared well, and is superior to the traditional Grey prediction model, moreover this method also applied to the situation of original data was few or the historical data had transition. The forecasting results from this method can be more reliable and accurate, so it can instruct the practice accurately

Download Full-text

Application of MBR Membrane Flux Prediction Based on Elman Neural Network

DEStech Transactions on Engineering and Technology Research ◽

10.12783/dtetr/iccere2017/18308 ◽

2018 ◽

Author(s):

Chunqing Li ◽

Xinchang Wang

Keyword(s):

Neural Network ◽

Elman Neural Network ◽

Membrane Flux

Download Full-text

Landslide Displacement Prediction Method Based on GA-Elman Model

Applied Sciences ◽

10.3390/app112211030 ◽

2021 ◽

Vol 11 (22) ◽

pp. 11030

Author(s):

Chenhui Wang ◽

Yijiu Zhao ◽

Libing Bai ◽

Wei Guo ◽

Qingjia Meng

Keyword(s):

Neural Network ◽

Prediction Model ◽

Monitoring Data ◽

Mountainous Area ◽

Elman Neural Network ◽

Displacement Prediction ◽

Dynamic Memory ◽

Landslide Displacement Prediction ◽

Neural Network Prediction ◽

Network Prediction

The deformation process of landslide displacement has complex nonlinear characteristics. In view of the problems of large error, slow convergence and poor stability of the traditional neural network prediction model, in order to better realize the accurate and effective prediction of landslide displacement, this research proposes a landslide displacement prediction model based on Genetic Algorithm (GA) optimized Elman neural network. This model combines the GA with the Elman neural network to optimize the weights, thresholds and the number of hidden neurons of the Elman neural network. It gives full play to the dynamic memory function of the Elman neural network, overcomes the problems that a single Elman neural network can easily fall into local minimums and the neuron data is difficult to determine, thereby effectively improving the prediction performance of the neural network prediction model. The displacement monitoring data of a slow-varying landslide in the Guizhou karst mountainous area are selected to predict and verify the landslide displacement, and the results are compared with the traditional Elman neural network prediction results. The results show that the prediction results of GA-Elman model are in good agreement with the actual monitoring data of landslide. The average error of the model is low and the prediction accuracy is high, which proves that the GA-Elman model can play a role in the prediction of landslide displacement and can provide reference for the early warning of landslide displacement deformation.

Download Full-text

Prediction Model of Coal-Fired Power Plant Boiler's Nitrogen Oxide Emissions Based on Elman Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.807-809.227 ◽

2013 ◽

Vol 807-809 ◽

pp. 227-231 ◽

Cited By ~ 1

Author(s):

Ying Chen ◽

Zhong Guang Fu ◽

Xiao Hang Lv

Keyword(s):

Neural Network ◽

Power Plant ◽

Prediction Model ◽

Thermal Power ◽

Atmospheric Environment ◽

Nox Emissions ◽

Elman Neural Network ◽

New Ideas ◽

Mechanism Modeling ◽

Neural Network Theory

Nitrogen oxides are dangerous toxic pollutants to human health and the atmospheric environment.[ Boilers NOx emissions as a major source of energy conservation is the most important task. The generation principles and influencing factors of coal-fired power plant boilers NOx were discussed. The current mechanism modeling had limitations and shortcomings, by studying reversed modelings and artificial neural network theory, Elman neural network was used to build the prediction model of coal-fired boilers NOx emissions. By comparing the predicted results with the true results, the convergence speed and accuracy of the method are both satisfactory to provide reference and guidance, and it provides new ideas and new ways for thermal power NOx measurements.

Download Full-text