Error prediction for neural networks by fuzzification

Abstract

In classical crisp neural networks the output cannot be estimated for arbitrary input data. This situation can be overcome if fuzzy neural nets are trained with fuzzy data. These continuous data often better describe certain situations. Because fuzzy neural networks map fuzzy numbers to fuzzy numbers, a criterion for choosing a good training set can be formulated. Together with an important fuzzy neural network property, the output for arbitrary crisp input data can be estimated based on the fuzzy training set.