Newspaper demand prediction and replacement model based on fuzzy clustering and rules
Information Sciences: an International Journal
Fuzzy Sets and Systems
Electric load forecasting based on locally weighted support vector regression
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A neural-fuzzy modelling framework based on granular computing: Concepts and applications
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
| Hi-index | 0.00 |
A class of neurofuzzy networks and a constructive, competition-based learning procedure is introduced. Given a set of training data, the learning procedure automatically adjusts the input space portion to cover the whole space and finds membership functions parameters for each input variable. The network processes data following fuzzy reasoning principles and, due to its structure, it is dual to a rule-based fuzzy inference system. The neurofuzzy model is used to forecast seasonal streamflow, a key step to plan and operate hydroelectric power plants and to price energy. A database of average monthly inflows of three Brazilian hydroelectric plants located at different river basins was used as source of training and test data. The performance of the neurofuzzy network is compared with period regression, a standard approach used by the electric power industry to forecast streamflows. Comparisons with multilayer perceptron, radial basis network and adaptive neural-fuzzy inference system are also included. The results show that the neurofuzzy network provides better one-step-ahead streamflow forecasting, with forecasting errors significantly lower than the other approaches.