Performance of neural networks in managerial forecasting
International Journal of Intelligent Systems in Accounting and Finance Management - Special issue on neural networks
Neural networks in applied statistics
Technometrics
Neural network models for time series forecasts
Management Science
Adaptive non-parametric efficiency frontier analysis: a neural-network-based model
Computers and Operations Research
Evaluating power plant efficiency: a hierarchical model
Computers and Operations Research
A Meta heuristic approach for performance assessment of production units
Expert Systems with Applications: An International Journal
Generalization performance of support vector machines and neural networks in runoff modeling
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Effective diagnosis of heart disease through neural networks ensembles
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Neural networks for cost estimation of shell and tube heat exchangers
Expert Systems with Applications: An International Journal
Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network
Expert Systems with Applications: An International Journal
Blind source separation with dynamic source number using adaptive neural algorithm
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This study proposes a non-parametric efficiency frontier analysis method based on artificial neural network (ANN) for measuring efficiency as a complementary tool for the common techniques of the efficiency studies in the previous studies. The proposed ANN algorithm is able to find a stochastic frontier based on a set of input-output observational data and do not require explicit assumptions about the functional structure of the stochastic frontier. Furthermore, it uses a similar approach to econometric methods for calculating the efficiency scores. Moreover, the effect of the return to scale of decision making unit (DMU) on its efficiency is included and the unit used for the correction is selected based on its scale (under constant return to scale assumption). However, the proposed algorithm is capable of handling outliers and noise. This is shown by two examples related to outlier situations. It is also capable of performing optimization analysis and forecasting for a given set of data. The proposed approach is applied to a set of actual conventional power plants to show its applicability and superiority.