Publication Date:
2021-10-28
Description:
This study discusses how to fuzzify a feedforward neural network (FNN) to generate a fuzzy forecast that contains the actual value, while minimizing the average range of fuzzy forecasts. This topic has rarely been investigated in past studies but is an essential step to constructing a precise fuzzy FNN (FFNN). Existing methods fuzzify all parameters at the same time, which results in a nonlinear programming (NLP) problem that is not easy to solve. In contrast, in this study, the parameters of a FNN are fuzzified independently. In this way, the optimal values of fuzzy parameters can be derived theoretically. An illustrative example is used to illustrate the applicability of the proposed methodology. According to experimental results, fuzzyifying some parameters may not guarantee that all fuzzy forecasts contain the corresponding actual values. In addition, fuzzifying parameters closer to the output node is more likely to achieve a 100% hit rate. The results lay a foundation for establishing a precise deep FFNN in the future.
Electronic ISSN:
2075-1680
Topics:
Mathematics
