Research on prediction of internet public opinion based on grey system theory and fuzzy neural network

2018 ◽  
Vol 35 (1) ◽  
pp. 325-332 ◽  
Author(s):  
H. He
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Public Opinion ◽  
Fuzzy Neural Network ◽  
Grey System Theory ◽  
Grey System ◽  
Fuzzy Neural

Study on Deformation Prediction of Landslide Based on Grey Theory and BP Neural Network

2012 ◽  
Vol 204-208 ◽  
pp. 520-525 ◽  
Author(s):  
Rong Yu Li ◽  
Yong Fen Ruan ◽  
Shi Sheng Li ◽  
Yong Hong Wu
Keyword(s):  
Neural Network ◽  
System Theory ◽  
Bp Neural Network ◽  
Theory Model ◽  
Grey System Theory ◽  
Practical Significance ◽  
Grey Theory ◽  
Grey System ◽  
Engineering Project ◽  
Landslide Deformation

The stability of the landslide can be effectively evaluated and predicted by predicting the future development of landslide deformation according to the actual deformation of the landslide. Therefore, the accuracy of the prediction regarding the landslide deformation determines the validity of the landslide stability assessment. The GM (1.1) model in the grey system theory, uses displacement time series to establish the grey differential equation. By solving the equation, we can obtain a time response function, which can then be used to predict the landslide deformation. The BP neural network is a used for training and exercising on the deformed samples. After the error meets the requirement, we can then use the trained model to predict the landslide deformation. This paper use both grey system theory model and BP neural network model to predict Jinlong ditch application field landslide deformation.The prediction results are compared and analyzed to test the accuracy of these two predictions. Finding a more accurate prediction method for application in actual engineering project has practical significance.

A fuzzy neural network approach for modeling the growth kinetics of FeB and Fe2B layers during the boronizing process

2018 ◽  
Vol 106 (6) ◽  
pp. 603 ◽  
Author(s):  
Bendaoud Mebarek ◽  
Mourad Keddam
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Fuzzy Neural Network ◽  
Treatment Time ◽  
Calculation Model ◽  
Average Error ◽  
Network Approach ◽  
Neural Network Approach ◽  
Fuzzy Neural ◽  
Kinetics Of

In this paper, we develop a boronizing process simulation model based on fuzzy neural network (FNN) approach for estimating the thickness of the FeB and Fe2B layers. The model represents a synthesis of two artificial intelligence techniques; the fuzzy logic and the neural network. Characteristics of the fuzzy neural network approach for the modelling of boronizing process are presented in this study. In order to validate the results of our calculation model, we have used the learning base of experimental data of the powder-pack boronizing of Fe-15Cr alloy in the temperature range from 800 to 1050 °C and for a treatment time ranging from 0.5 to 12 h. The obtained results show that it is possible to estimate the influence of different process parameters. Comparing the results obtained by the artificial neural network to experimental data, the average error generated from the fuzzy neural network was 3% for the FeB layer and 3.5% for the Fe2B layer. The results obtained from the fuzzy neural network approach are in agreement with the experimental data. Finally, the utilization of fuzzy neural network approach is well adapted for the boronizing kinetics of Fe-15Cr alloy.

Prediction of Technical Reserves Based on Grey Model — GM(1,1): Evidence from Non-life Egyptian Insurance Market

2019 ◽  
Vol 10 (9) ◽  
pp. 852-860
Author(s):  
Mahmoud Elsayed ◽  
◽  
Amr Soliman ◽  
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
Insurance Market ◽  
Grey Model ◽  
Loss Ratio ◽  
Grey System ◽  
Mathematical Technique ◽  
Supervisory Authority ◽  
Error Ratio ◽  
Using Data

Grey system theory is a mathematical technique used to predict data with known and unknown characteristics. The aim of our research is to forecast the future amount of technical reserves (outstanding claims reserve, loss ratio fluctuations reserve and unearned premiums reserve) up to 2029/2030. This study applies the Grey Model GM(1,1) using data obtained from the Egyptian Financial Supervisory Authority (EFSA) over the period from 2005/2006 to 2015/2016 for non-life Egyptian insurance market. We found that the predicted amounts of outstanding claims reserve and loss ratio fluctuations reserve are highly significant than the unearned premiums reserve according to the value of Posterior Error Ratio (PER).

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