Applying Rough Set Theory to Establish Artificial Neural Networks Model for Short Term Incidence Rate Forecasting

2013 ◽  
Vol 347-350 ◽  
pp. 2937-2941 ◽  
Author(s):  
Xiang Yu Zhao ◽  
Liang Liang Ma
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Incidence Rate ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Forecast Model ◽  
Short Term ◽  
Artificial Neural ◽  
Input Variables

Choosing input variable and networks architecture are key processes for modeling short term incidence rate forecast by artificial neural networks, in this paper a method based on rough set theory is proposed to deal with them. In the proposed approach, the key factors that affect the incidence rate forecasting are firstly identified by rough set theory and then the input variables of forecast model can be determined. On the basis of the process mentioned above a set of influence rules can been obtained through reductive mining process of attributes and attribute values, then a neural networks of incidence rate forecast model is established on the rule set and BP-algorithm is adopt to optimize the networks. The method indicates that incidence rate forecast model can be established according some theoretical principles and avoiding blindness. A practical application is given at last to demonstrate the usefulness of the novel method.

scholarly journals The Impact of Macroeconomic Factors on Residential Property Price Indices in Europe

2012 ◽  
Vol 12 (2) ◽  
pp. 103-125 ◽  
Author(s):  
Małgorzata Renigier-Biłozor ◽  
Radosław Wiśniewski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
European Countries ◽  
Residential Property ◽  
Price Indices ◽  
Property Price ◽  
Property Markets

Abstract This paper aims to determine the influence of selected variables on residential property price indices for the European countries, with particular attention paid to Italy and Poland, using a rough set theory and an approach that uses a committee of artificial neural networks. Additionally, the overall analysis for each European country is presented. Quarterly time series data constituted the material for testing and empirical results. The developed models show that the economic and financial situation of European countries affects residential property markets. Residential property markets are connected, despite the fact that they are situated in different parts of Europe. The economic and financial crisis of countries has variable influence on prices of real estate. The results also suggest that methodology based on the rough set theory and a committee of artificial neural networks has the ability to learn, generalize, and converge the residential property prices index.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

scholarly journals Modelling and Prediction of Monthly Global Irradiation Using Different Prediction Models

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2332
Author(s):  
Cecilia Martinez-Castillo ◽  
Gonzalo Astray ◽  
Juan Carlos Mejuto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Random Forests ◽  
Prediction Models ◽  
Meteorological Station ◽  
Ann Model ◽  
Global Irradiation ◽  
Artificial Neural ◽  
Input Variables ◽  
Using Data

Different prediction models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to model the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best model was checked in two independent stations. The results obtained confirmed that the best methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 1226 kJ/(m2∙day) and 1136 kJ/(m2∙day), respectively, and predict conveniently for independent stations, 2013 kJ/(m2∙day) and 2094 kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Short-term fading simulation using artificial neural networks

2011 ◽  
Vol 65 (7) ◽  
pp. 641-649 ◽  
Author(s):  
Nikola Tomasevic ◽  
Aleksandar Neskovic ◽  
Natasa Neskovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term ◽  
Artificial Neural
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