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On the Adequacy of a Takagi–Sugeno–Kang Protocol as an Empirical Identification Tool for Sigmoidal Allometries in Geometrical Space

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Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 940))

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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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  1. Centro de Investigación Científica y de Educación Superior de Ensenada, Carretera Ensenada-Tijuana, No 3918, Zona Playitas, 22860, Ensenada, BC, Mexico

    Cecilia Leal-Ramírez & Héctor Echavarría-Heras

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  1. Cecilia Leal-Ramírez
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  2. Héctor Echavarría-Heras
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    Oscar Castillo

  2. Division of Graduate Studies and Research, Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

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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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