A fuzzy-neural approach for global CO2 concentration forecasting

Authors:
Toly Chen;Yi-Chi Wang
Affiliations:
Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan;Department of Industrial Engineering and Systems Management, Feng Chia University, Taichung, Taiwan
Venue:
Intelligent Data Analysis
Year:
2011

Citing 11
Cited 1

Possibilistic linear systems and their application to the linear regression model

Fuzzy Sets and Systems
Fuzzy linear regression with fuzzy intervals

Fuzzy Sets and Systems
Properties of certain fuzzy linear regression methods

Fuzzy Sets and Systems
An efficient approach for large scale project planning based on fuzzy Delphi method

Fuzzy Sets and Systems
Fuzzy Mathematical Models in Engineering and Management Science

Fuzzy Mathematical Models in Engineering and Management Science
Evaluating the mid-term competitiveness of a product in a semiconductor fabrication factory with a systematic procedure

Computers and Industrial Engineering
An intelligent mechanism for lot output time prediction and achievability evaluation in a wafer fab

Computers and Industrial Engineering
Fuzzy-neural approaches with example post-classification for estimating job cycle time in a wafer fab

Applied Soft Computing
A fuzzy-neural knowledge-based system for job completion time prediction and internal due date assignment in a wafer fabrication plant

International Journal of Systems Science
Parameterized defuzzification with maximum entropy weighting function-Another view of the weighting function expectation method

Mathematical and Computer Modelling: An International Journal
Applying a Fuzzy and Neural Approach for Forecasting the Foreign Exchange Rate

International Journal of Fuzzy System Applications

A methodological approach to mining and simulating data in complex information systems

Intelligent Data Analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

The global CO2 concentration is considered to be one of the most important causes of global warming that must be closely monitored, accurately forecasted, and controlled as good as possible. To accurately forecast the global CO2 concentration, a hybrid fuzzy linear regression FLR and back propagation network BPN approach is proposed in this study. In this proposed approach, multiple experts construct their own FLR equations from various viewpoints to forecast future global CO2 concentrations. Each FLR equation can be converted into two equivalent nonlinear programming problems to be solved. To combine these fuzzy forecasts, a two-step aggregation mechanism is applied. At the first step, fuzzy intersection is applied to combine the fuzzy global CO2 concentration forecasts into a polygon-shaped fuzzy number, in order to improve the precision. After that, a BPN is constructed to defuzzify the polygon-shaped fuzzy number and to generate a representative/crisp value, so as to enhance the accuracy. Some historical data on global CO2 concentrations were used to evaluate the effectiveness of the proposed methodology. According to the experimental results, the proposed methodology improved both the precision and the accuracy of forecasting the global CO2 concentration by 28% and 91%, respectively.