Abstract
The knowledge embedded in Rule-Base systems, derived either from human experts or from clustering algorithms, is most of the times inconsistent due to interpersonal differences on the definition of the rule’s membership functions or incomplete in some regions of the input/output space as a result from operation conditions not experienced during a model’s training stage. The theory of Type-2 FLS focus on the mitigation of these problems and is already proving its advantages comparatively to alternative approaches already well established in literature.
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Antão, R., Mota, A., Escadas Martins, R., Tenreiro Machado, J. (2017). Conclusions. In: Type-2 Fuzzy Logic. Nonlinear Physical Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-4633-9_7
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DOI: https://doi.org/10.1007/978-981-10-4633-9_7
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Online ISBN: 978-981-10-4633-9
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