Meta fuzzy functions based feed-forward neural networks with a single hidden layer for forecasting

Abstract

Feed-forward neural networks have been frequently used in forecasting problems, recently. In this study, we propose a naive method to improve the forecasting ability of feed-forward neural networks with a single hidden layer by adapting meta fuzzy functions. Because neural networks are very sensitive to the initial random weights, usually some numbers of repeats are processed with different initial random weights. The forecasts for the different repeats are, then, averaged with equal weights to obtain more reliable results. However, if we can assign the correct initials with more appropriate weights, then, neural networks can produce very competitive outcomes. In this sense, rather than assigning the equal weights for different repeats with different initials, meta fuzzy functions are used to investigate the best/better forecast with assigning different weights. 4 datasets are used to verify the performance of the proposed method in terms of RMSE and MAPE metrics.