Learning rules for Sugeno ANFIS with parametric conjunction operations

Cortés-Antonio, Prometeo; Batyrshin, Ildar; Martínez-Cruz, Alfonso; Villa-Vargas, Luis Alfonso; Salinas, Marco Antonio Ramírez; Rudas, Imre J.; Castillo, Oscar; Molina-Lozano, Herón

doi:10.1016/j.asoc.2020.106095

Cited by 18 publications

(8 citation statements)

References 43 publications

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“…Sugeno FIS has fuzzy inputs as Mamdani but it does not need the defuzzification step. The Sugeno FIS outputs membership functions (MF) are linear or constant, so Sugeno outputs are crisp values [ 35 ]. Thus Fuzzy-Sugeno is to reduce the number of rules required by the Mamdani model [ 36 ].…”

Section: Methodsmentioning

confidence: 99%

A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

Gökler

Yılmaz²,

Ürük³

et al. 2022

Heliyon

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Section: Methodsmentioning

confidence: 99%

A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

Gökler

Yılmaz²,

Ürük³

et al. 2022

Heliyon

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“…Jang [18] proposed a prediction method comprising ANN and fuzzy system which is ANFIS. The capability of predicting nonlinear and complex behavior of the phenomena motivated researchers to employ this method in order to describe the characteristics of various systems [19][20][21]. Different types and numbers of membership functions were utilized to have excellent structure of ANFIS model with the greatest accuracy.…”

Section: Adaptive Network-based Fuzzy Inference System (Anfis)mentioning

confidence: 99%

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

et al. 2021

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This study investigated the predicted cutting force model of a turning operation for Al–Si–Cu cast alloy modified with modifiers based on adaptive neuro-fuzzy inference system (ANFIS) approach. Feed rate, cutting speed and Silicon spacing were considered as the input parameters. A series of turning experiments were conducted at various feed rates and cutting speeds. The prediction result showed that the ANFIS model successfully predicted the cutting force value in terms of cutting speed, feed rate and Si spacing. A mathematical model was proposed to describe the cutting force changes during the machining of Al–Si–Cu cast alloy. Moreover, the addition of Bismuth into the base alloy decreased the cutting force compared to other refinement elements.

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“…25 The ANFIS methodology is a sophisticated hybrid artificial intelligence technology that integrates the learning capabilities of artificial neural networks with the characteristics of fuzzy interface systems. 26,27 The ANFIS technique allows for learning from training data supplied during input and mapping out solutions on a fuzzy interface system. Since the hidden layers of the ANFIS network are set by a fuzzy interface system, it has better prediction skills than ANN.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the mechanical properties of heat-treated fused filament fabrication thermoplastics using adaptive neuro-fuzzy inference system

Pazhamannil,

Hadidi,

Edacherian

et al. 2023

Journal of Thermoplastic Composite Materials

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The requirement for post-processing methods in 3D printing has increased due to its major limitations such as poor mechanical and surface properties. Thermal annealing, a heat-treatment procedure, has proven to be an excellent approach for increasing the mechanical strength of additive manufactured thermoplastics. The optimized thermal annealing parameters for maximum tensile strength are identified. The Adaptive Neuro-fuzzy Inference System (ANFIS) methodology is employed in this study to forecast the tensile strength of thermal annealed fused filament fabricated polylactic acid (PLA), carbon fiber filled polylactic acid (PLA-CF), and polycarbonate acrylonitrile butadiene styrene (PC-ABS). The root mean square error (RMSE) of the predicted tensile strength of PLA, PLA-CF, and PC-ABS are obtained as 1.012, 0.5, and 0.835 respectively. The coefficient of determination for the predicted model of thermoplastics PLA, PLA-CF, and PC-ABS is determined as 0.98, 0.99, and 0.89 respectively. The ANFIS model developed is successful in determining the tensile strength of annealed 3D printed thermoplastics at various annealing conditions of temperature and duration. Further, the study investigates the effect of heat treatment on the compressive, impact, and flexural properties of PLA prints.

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Learning rules for Sugeno ANFIS with parametric conjunction operations

Cited by 18 publications

References 43 publications

A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

A new hybrid risk assessment method based on Fine-Kinney and ANFIS methods for evaluation spatial risks in nursing homes

Neuro-fuzzy based predictive model for cutting force in CNC turning process of Al–Si–Cu cast alloy using modifier elements

Prediction of the mechanical properties of heat-treated fused filament fabrication thermoplastics using adaptive neuro-fuzzy inference system

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