ANFIS

(redirected from Adaptive Neuro-Fuzzy Inference System)
AcronymDefinition
ANFISAdaptive Network-Based Fuzzy Inference System
ANFISAssociazione Nazionale dei Formatori Insegnanti Supervisori (Italian: National Association of Teacher Educators Supervisors)
ANFISAdaptive Neuro-Fuzzy Inference System
ANFISAdaptive Network Fuzzy Inference System
References in periodicals archive ?
[8.] Yildiz, A., Akin, M., Poyraz, M., and Kirbas, G., "Application of Adaptive Neuro-fuzzy Inference System for Vigilance Level Estimation by Using Wavelet-Entropy Feature Extraction," Expert Systems with Applications 36:7390-7399, 2009, doi:10.1016/j.eswa.2008.09.003.
Adaptive Neuro-Fuzzy Inference System For Subjectivity Detection
Khademi, "A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system," Iran University of Science and Technology, vol.
In this paper, we propose a solution to the problem of gait phase detection for diagnosis or use in the control system of the lower limb exoskeleton in form of adaptive neuro-fuzzy inference systems. The proposed adaptive system is based on an artificial neural network, in which neurons are defined and linked together in such a way as to simulate a fuzzy reasoning system [7].
Han, "Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques," Advances in Water Resources, vol.
Artificial intelligent systems such as artificial neural networks (ANN), fuzzy inference system (FIS), and adaptive neuro-fuzzy inference system (ANFIS) have been applied to model a wide range of challenging problems in science and engineering.
In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm.
General overview of adaptive neuro-fuzzy inference system (ANFIS)
Using a real data set from a healthcare organization, we address this important research gap by examining the performance of an adaptive neuro-fuzzy inference system (ANFIS) with semi-supervised learning (SSL) in improving debt recovery rate.
Aiming at optimizing such systems (Shiraz, Gani, Hafeez, & Buyya, 2012; Anuar, Sallehudin, Gani, & Zakari, 2008; Mansoori, Zakaria, & Gani, 2012; Enayatifar, Sadaei, Abdullah, & Gani, 2013) to ensure optimal investment in the wind farm, the adaptive neuro-fuzzy inference system (ANFIS) method is used (Mohandes, Rehman, & Rahman, 2012; Ata, Kocyigit, & Kocyigit, 2010; Jang, 1993; Shamshirband, Anuar, Kiah, & Patel, 2013).
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