科研之友
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摘要
This study presents a hybrid neural network fuzzy mathematical programming approach for improvement of natural gas price estimation in industrial sector. It is composed of artificial neural network (ANN), fuzzy linear regression (FLR), and conventional regression (CR). The preferred FLR, ANN, and CR models are selected via mean absolute percentage of error. The intelligent approach of this study is then applied to estimate natural gas price in industrial sector. Domestic sector is also used to further show the flexibility and applicability of the hybrid approach. The economic indicators used in this paper are consumer price index, population, gross domestic and annual natural gas consumption. The stated indicators could be contaminated with noise and vagueness. Moreover, there is a need to develop a hybrid approach to deal with both noise and vagueness. The input data were divided into train and test datasets. A complete sensitivity analysis has been performed by changing train and test data to show the superiority of the proposed approach. The superiority of ANN for the domestic sector and FLR for the industrial sector was proved by error analysis. The results showed that different models may be selected as preferred model, in different cases and situations. The proposed approach of this study would help policy makers to effectively manage natural gas price in vague, noisy, and complex manufacturing sectors. This is the first study that presents a hybrid approach for estimating the natural gas price in industrial sector with possible noise, non-linearity, and uncertainty.
- 出版日期2012-9
