M
Muhammet Unal
Researcher at Marmara University
Publications - 27
Citations - 564
Muhammet Unal is an academic researcher from Marmara University. The author has contributed to research in topics: Artificial neural network & Structural health monitoring. The author has an hindex of 11, co-authored 25 publications receiving 490 citations. Previous affiliations of Muhammet Unal include Gazi University.
Papers
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Journal ArticleDOI
Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network
TL;DR: In this paper, the authors proposed an artificial neural network (ANN) based fault estimation algorithm was verified with experimental tests and promising results, every test was initiated with a reference ANN architecture to avoid inappropriate classification during the evaluation of fitness value.
Book ChapterDOI
Artificial Neural Networks
TL;DR: An Artificial Neural Network (ANN) as discussed by the authors is an information processing system that has certain performance characteristics in common with biological neural networks, such as it can be considered as threshold units that fire when their total input exceeds certain bias levels.
Book
Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
TL;DR: This book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters.
Journal ArticleDOI
Contact and non-contact approaches in load monitoring applications using surface response to excitation method
Shervin Tashakori,Amin Baghalian,Muhammet Unal,Hadi Fekrmandi,Volkan Y. Senyurek,Dwayne McDaniel,Ibrahim N. Tansel +6 more
TL;DR: In this paper, the performance of the surface response to excitation (SuRE) method was evaluated with the conventional piezoelectric elements and scanning laser vibrometer used as contact and non-contact sensors, respectively, for monitoring the presence of loads on the surface.
Journal ArticleDOI
A novel approach for classification of loads on plate structures using artificial neural networks
TL;DR: In this paper, the location of applied load on an aluminum and a composite plate was identified using two type of neural network classifiers: Multi-Layer Perceptron (MLP) and Radial Basis Function (RBF) classifiers.