A Hybridized Forecasting Method Based on Weight Adjustment of Neural Network Using Generalized Type-2 Fuzzy Set

Abstract

This paper proposes a hybridized forecasting method on weight adjustment of neural networks with back-propagation learning using general type-2 fuzzy sets. Initialization of weights and their adjustment in neural network are important areas of research as it increases the computation speed to get the optimized result. Higher order fuzzy logic systems are able to deal with the high levels of uncertainties present in the majority of real-world problems. Here, the concept of interval-based zSlices has been used to obtain the general type-2 fuzzy weights for the neural network architecture. We implement this proposed methodology on benchmark Mackey–Glass time series data (for $$\tau =17$$ ) and present a comparison of the results obtained with our approach and those of the existing approaches. The method is also applied on enrollment data of University of Alabama, closing price index of Shenzhen stock exchange, closing price index of Shanghai stock exchange, Canadian lynx data, and the results are presented.