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
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Five models were proposed for modeling emotions from Arousal and Valence: a model to determine the strongest emotion that is felt by the person (called the Strongest Emotion model and based on a model used by Mandryk and Atkins to determine emotions while playing an interactive game), a Mamdani's model and a Takagi-Sugeno's model (both called a rule-based FIS because of the structure of their respective rules), which calculates the angle and the intensity of the emotion and two ANFIS using a grid partition and a fuzzy clustering technique.
The training data set was used to train the ANFIS, whereas the testing data set was used to verify the accuracy and the effectiveness of the trained ANFIS model for the detection of DDoS attack in VKC.
In general terms, an ANFIS is an algorithm that automatically adjusts Sugeno type fuzzy logic membership functions with the aid of training data.
Keywords: bad debt recovery, healthcare industry, adaptive neuro-fuzzy inference system (ANFIS), semi-supervised learning, classification
The ANFIS graphical outputs show the training points in Figure 1 and testing points in Figure 2.
In this paper, ANFIS soft computing technique is applied with the aim of developing an on-line voltage stability evaluation model.