Abstract
Examining sigmoidal allometries in geometrical space can be carried away by direct nonlinear regression or generalized additive modeling approaches. Nevertheless, producing consistent estimates of breakpoints characterizing phases composing sigmoidal heterogeneity could be problematic. Here, we explain how the paradigm of weighted multiple–phase allometries embraced by the mixture structure of the total output of a first-order Takagi–Sugeno–Kang fuzzy model can carry on this task in a direct, intuitive and efficient way. Present calibration tasks relied on log-transformed amniote testes mass allometry data. The considered TSK fuzzy model approach not only offers a way to back the assumption that analyzed testes mass allometry is sigmoidal in geometrical space but beyond this, it provided meaningful estimates for transition among involved phases. Results confirm previously raised views on the superior capabilities of the addressed fuzzy approach to validating prior subjective knowledge in allometry.
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Leal-Ramírez, C., Echavarría-Heras, H. (2021). On the Adequacy of a Takagi–Sugeno–Kang Protocol as an Empirical Identification Tool for Sigmoidal Allometries in Geometrical Space. In: Castillo, O., Melin, P. (eds) Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications. Studies in Computational Intelligence, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-68776-2_19
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