Parallel Neural Fuzzy-Based Joint Classifier Model for Grading Autistic Disorder

2015 ◽  
pp. 13-26 ◽  
Author(s):  
Anju Pratap ◽  
C. S. Kanimozhiselvi ◽  
R. Vijayakumar ◽  
K. V. Pramod
Keyword(s):  
Autistic Disorder ◽  
Neural Fuzzy

Perinatal Differences Between Youth With Autistic Disorder and Asperger's Disorder

2013 ◽  
Author(s):  
Amy E. Zimmerman ◽  
John M. Starling ◽  
Raymond S. Dean
Keyword(s):  
Autistic Disorder ◽  
Asperger’S Disorder ◽  
Asperger's Disorder

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.

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