Course Information
| Course Unit Title |
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NEURAL NETWORKS APPLICATIONS IN ENGINEERING |
| Course Unit Code |
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01INS9614 |
| Type of Course Unit |
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Optional |
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Level of Course Unit
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Second Cycle |
| Year of Study
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Preb |
| Semester
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255.Semester |
| Number of ECTS Credits Allocated
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6,00 |
| Name of Lecturer(s) |
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---
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| Course Assistants |
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---
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| Learning Outcomes of The Course Unit |
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Learning of neural networks
MATLAB-based model by the method of artificial neural networks to improve
Apply the method of artificial neural networks to engineering problems
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| Mode of Delivery |
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Face-To-Face
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| Prerequisities and Co-requisities Courses |
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Unavailable
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| Recommended Optional Programme Components |
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Unavailable
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| Course Contents |
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Introduction to neural networks. Multi-layer artificial neural networks, training algorithms, modeling and engineering applications of the principles. Radial-based artificial neural networks, multi-layered artificial neural networks and applications according to their strengths and shortcomings. Examples of engineering application.
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Languages of Instruction |
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Turkish
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| Course Goals |
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Artificial neural networks apply to engineering problems
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Course Aims |
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Increasing the use of artificial neural networks in engineering
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WorkPlacement |
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Recommended or Required Reading
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Textbook
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Artificial Neural Networks Applications lecture notes (prepared by Kemal SAPLIOĞLU)
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Additional Resources
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Çetin Elmas (2012), Yapay Zeka Uygulamaları, Yapay Sinir Ağları ? Bulanık Mantık?Genetik Algoritma, Ankara: Seçkin Yayinevi ISBN 9789750216961
Ercan Öztemel (2006), Yapay Sinir Ağları, Istanbul: Papatya ISBN 9789756797396
Yapay Sinir Ağları İlkeleri / Zekai Şen, Su Vakfı Yayınları
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Material Sharing
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Documents
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Assignments
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Exams
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Additional Material
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Planned Learning Activities and Teaching Methods
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Lectures, Practical Courses, Presentation, Seminar, Project, Laboratory Applications (if necessary)
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ECTS / Table Of Workload (Number of ECTS credits allocated)
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Student workload surveys utilized to determine ECTS credits.
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Activity
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Course Duration (Excluding Exam Week)
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Time Of Studying Out Of Class
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Homeworks
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Presentation
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Project
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Lab Study
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Field Study
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Visas
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Finals
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Workload Hour (30)
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Total Work Charge / Hour
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Course's ECTS Credit
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Assessment Methods and Criteria
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Studies During Halfterm
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Visa
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Quiz
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Homework
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Attendance
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Application
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Lab
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Project
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Workshop
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Seminary
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Field study
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TOTAL
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The ratio of the term to success
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The ratio of final to success
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TOTAL
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Weekly Detailed Course Content
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Week
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Topics
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1
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Introduction to neural networks
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2
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Multi-layer artificial neural networks
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3
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Multi-layer artificial neural network applications
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4
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Training algorithms
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5
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Applications training algorithms
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6
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Examples of model development
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7
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A Matlab-based applications
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8
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A Matlab-based applications and solutions
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9
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Regression analysis
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10
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Compliance test data
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11
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General applications
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12
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Evaluating the results obtained
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13
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Evaluation and application of the results obtained
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14
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General evaluation
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