Comparative analysis on the application of neuro‐fuzzy models for complex engineered systems: Case study from a landfill and a boiler

Abstract

This work aims at developing an explicit neuro‐fuzzy (NF) model to characterize complex engineered systems associated with high nonlinearity, uncertainties, and multivariable couplings. The NF model synergistically exploits the advantages of fuzzy belongingness of each input variable to all output variables and learning ability of neural networks. Owing to the inherent complexities associated with 2 complex engineered systems, a landfill and a boiler were selected to develop models that provide intelligent decisions for optimizing the operational parameters. Data compiled from field‐scale investigation/real plant operation involving various operating scenarios were used to develop the models. Predicting capability of the developed models was evaluated through the correlation coefficient and mean absolute percentage error values. Superiority of the proposed NF model to other similar models has been justified and demonstrated.

Stability analysis in identification of interval type‐2 adaptive neuro‐fuzzy inference system: Contribution to a novel Lyapunov function

“…T2FS and T1FS have been addressed in numerous studies in the fields of engineering and control (Chaoui, Khayamy, & Aljarboua, ; Coteli, Acikgoz, Ucar, & Dandil, ; Mendez, Hernández, Cavazos, & Mata‐Jiménez, ), identification (Almaraashi, John, Coupland, & Hopgood, ; Dahal, Almejalli, Hossain, & Chen, ; Togun & Baysec, ), prediction (Mendez et al, ; Teshnehlab, Shoorehdeli, & Sedigh, ), and modelling (Huang & Chen, ; Liu, Leng, & Fang, ; Meher, Behera, Rene, & Park, ). Identification of dynamic systems using ANFIS demands powerful algorithms to optimize the antecedent and the consequent parameters.…”