METHODOLOGY 3.1 Overview 3.2 Design of a Modified Adaptive Fuzzy Inference Engine MAFIE 3.2.1 Fuzzy Inference System FIS 3.2.2 Hybrid Fuzzy clustering algorithm for Automatic

Accordingly, three artificial intelligence (AI) techniques comprising adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and adaptive

Adaptive Neuro Fuzzy Inference System (ANFIS), a modified Fuzzy Inference System (FIS) works similar to that of Neural Networks. With the help of ANFIS, tuning of membership

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This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro -fuzzy inference system in the prediction of okra yield using

The neuro fuzzy classifier is used for classification is the Adaptive Network based Fuzzy Inference system(ANFIS).The developed neuro fuzzy classifier is tested

Adaptive Neuro Fuzzy Inference System (ANFIS) is a fuzzy mapping algorithm that is based on Tagaki-Sugeno-Kang (TSK) fuzzy inference system (Jang et al., 1997

The neuro fuzzy classifier is used for classification is the Adaptive Network based Fuzzy Inference system(ANFIS).The developed neuro fuzzy classifier is tested for

This study compared some basic soft computing techniques, namely, artificial neural network, fuzzy inference system and adaptive neuro-fuzzy inference system as

To monitor the engine conditions, an adaptive neuro-fuzzy inference system (ANFIS) is used to capture the nonlinear connections between the air-fuel ratio and

By the end of 2011 foreign investors have slightly reduced their stock of direct invest- ment into the German E&E industry by one percent (year over year) to 32.5 billion

In this study, Adaptive Neuro-Fuzzy Inference System (ANFIS) has been employed to determine the engine power, torque, brake specific fuel consumption (bsfc), and emission

number of rules and antecedent membership functions and then uses linear least squares. estimation to determine each rule’s

Therefore, Adaptive Neuro Fuzzy Inference System (ANFIS) as neuro-fuzzy classifier is applied to churn prediction modeling and benchmarked to traditional rule- based classifier

This thesis presents the design and analysis of Neuro Fuzzy controller based on Adaptive Neuro-Fuzzy inference system (ANFIS) architecture for Load frequency

Artificial Neural Network (ANN), Fuzzy Inference System (FIS) as well as Adaptive Neuro Fuzzy Inference System (ANFIS) are deployed to predict the remaining useful lifetime

Then, a classification framework based on entropy measures and adaptive neuro-fuzzy inference system (ANFIS) classifier is proposed to distinguish ESES and normal EEG signals..

The performance of evolving Neuro-Fuzzy model is compared with an Adaptive Neuro-Fuzzy Inference System (ANFIS) and a Multiple Regression Model (MRM) in term of accuracy