Fuzzy transform and ANN approaches for analyzing behavior of coal consumption in industrial sectors: The cases of 20 states and districts of Columbia and United States

Efficiency frontier analysis is an important approach of evaluating performance of different sectors in indsustry. This paper presents an adaptive neural network and fuzzy transform approach for the coal consumed by industrial sectors in 20 different US states including District of Columbia. The behavior of the coal consumption is assessed based on the data during 1960–2011. Despite of the strengths of these approaches, they have their own limitations. The proposed algorithms are able to find a stochastic frontier based on a set of observational input–output data and explicit that assumptions about the functional structure of the stochastic frontier are not needed. By applying these algorithms and calculating the efficiency scores, the ranking between decision-making units (DMUs) will be available. The superiority and advantages of adaptive neural network algorithm are shown by comparing its results against fuzzy transform algorithm.

Keywords:

neural network , fuzzy transform , industrial sectors , efficiency scores , DMU