Aoqi Xu1, Khalid A. Alattas2, Nasreen Kausar3, Ardashir Mohammadzadeh4, Ebru Ozbilge5,*, Tonguc Cagin5
Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 17-32, 2023, DOI:10.32604/iasc.2023.036623
- 29 April 2023
Abstract In many problems, to analyze the process/metabolism behavior, a model of the system is identified. The main gap is the weakness of current methods vs.
noisy environments. The primary objective of this study is to present a more
robust method against uncertainties. This paper proposes a new deep learning
scheme for modeling and identification applications. The suggested approach is
based on non-singleton type-3 fuzzy logic systems (NT3-FLSs) that can support
measurement errors and high-level uncertainties. Besides the rule optimization,
the antecedent parameters and the level of secondary memberships are also
adjusted by the suggested square More >
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