A Non-Singleton Type-3 Fuzzy Modeling: Optimized by Square-Root Cubature Kalman Filter

Abstract

In many problems, to analyze the process/metabolism behavior, a mod-el 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 root cubature Kalman filter (SCKF). In the learn-ing algorithm, the presented NT3-FLSs are deeply learned, and their nonlinear structure is preserved. The designed scheme is applied for modeling carbon cap-ture and sequestration problem using real-world data sets. Through various ana-lyses and comparisons, the better efficiency of the proposed fuzzy modeling scheme is verified. The main advantages of the suggested approach include better resistance against uncertainties, deep learning, and good convergence.