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This paper discusses the use of combined uncertainty methods in the computation of wavelets that best represent horse gait signals. Combined uncertainty computes a composite of two types of uncertainties, fuzzy and probabilistic. First, we introduce fuzzy uncertainty properties and classes. Next, the gait analysis problem is discussed in the context of correctly classifying wavelet-transformed sound gait from lame horse gait signals. Continuous wavelets are selected using generalized information theory-related concepts that are enhanced through the application of uncertainty management models. Our experimental results show that models developed by maximizing combined uncertainty produce better results, in terms of neural network correct classification percentage, compared to those computed using only fuzzy uncertainty.