Evaluating the Combined Effect of Temperature, Shear Rate and Water Content on Wild-Flower Honey Viscosity Using Adaptive Neural Fuzzy Inference System and Artificial Neural Networks

2013 ◽  
Vol 36 (4) ◽  
pp. 510-520 ◽  
Author(s):  
M.A. Al-Mahasneh ◽  
T.M. Rababah ◽  
A.S. Ma'Abreh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Shear Rate ◽  
Water Content ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Effect Of Temperature ◽  
Inference System ◽  
Temperature Shear ◽  
Neural Fuzzy

Modeling of cutting parameters in turning of PEEK composite using artificial neural networks and adaptive-neural fuzzy inference systems

2021 ◽  
pp. 089270572110130
Author(s):  
Gökçe Özden ◽  
Mustafa Özgür Öteyaka ◽  
Francisco Mata Cabrera
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Cutting Forces ◽  
Fuzzy Inference ◽  
Test Results ◽  
Inference System ◽  
Good Prediction Accuracy ◽  
Peek Composite ◽  
Artificial Neural ◽  
Neural Fuzzy

Polyetheretherketone (PEEK) and its composites are commonly used in the industry. Materials with PEEK are widely used in aeronautical, automotive, mechanical, medical, robotic and biomechanical applications due to superior properties, such as high-temperature work, better chemical resistance, lightweight, good absorbance of energy and high strength. To enhance the tribological and mechanical properties of unreinforced PEEK, short fibers are added to the matrix. In this study, Artificial Neural Networks (ANNs) and the Adaptive-Neural Fuzzy Inference System (ANFIS) are employed to predict the cutting forces during the machining operation of unreinforced and reinforced PEEK with30 v/v% carbon fiber and 30 v/v% glass fiber machining. The cutting speed, feed rate, material type, and cutting tools are defined as input parameters, and the cutting force is defined as the system output. The experimental results and test results that are predicted using the ANN and ANFIS models are compared in terms of the coefficient of determination ( R2) and mean absolute percentage error. The test results reveal that the ANFIS and ANN models provide good prediction accuracy and are convenient for predicting the cutting forces in the turning operation of PEEK.

scholarly journals Calculation of Resonant Frequency for a Microstrip Antenna with Vertical Slots Using Applying Adaptive Network-Based Fuzzy Inference System

2017 ◽  
Author(s):  
Mahmood Abbasi Layegh ◽  
Changiz Ghobadi ◽  
Javad Nourinia
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Resonant Frequency ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
High Approximation ◽  
Adaptive Network ◽  
Resonant Frequencies ◽  
Inference System ◽  
Artificial Neural

This paper attempts at applying adaptive network-based fuzzy inference system (ANFIS) for analysis of the resonant frequency of a microstrip rectangular patch antenna with two equal size slots which are placed on the patch vertically. The resonant frequency is calculated as the position of slots is shifted to the right and left sides on the patch. As a result , the antenna resonates at more than one frequency . Commonly, machine algorithms based on artificial neural networks are employed to recognize the whole resonant frequencies. However ,they fail to estimate the resonant frequencies correctly as in some cases variations are not very sensible and the resonant frequencies overlap each other . It can be concluded that artificial neural networks could be replaced in such designs by the adaptive network-based fuzzy Inference system due to its high approximation capability and much faster convergence rate.

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