erciyes university graduate school of natural and applied sciences

erciyes university graduate school of natural and applied sciences

ERCIYES UNIVERSITY
GRADUATE SCHOOL OF NATURAL AND
APPLIED SCIENCES
DEPARTMENT OF MECHATRONICS
ENGINEERING
I.
GENERAL INFORMATION
MEM 509 Artifical Neural Networks and Engineering Applications
Course Title
Semester: Fall
Language: Turkish
Local Credit (T-P-C) : 3-0-3
ECTS Credit: 7.0
Prof. Dr. Şahin Yıldırım
Instructor
09.00-17.00
Office Hour
Email: [email protected]
WEB Site: http://mekatronik.erciyes.edu.tr/syildirim
Faculty Contact Information:
Erciyes University, Faculty of Engineering, Dept. of Mechatronics Engineering, 38039 Kayseri,
TURKEY
Phone: 90 352 4374937 Extn.32953
Fax:
90 352 4374933
II.
COURSE INFORMATION
The Type and Level of Course
Elective: Yes
Must:
Core: Yes
Related:
Minor:
Advanced: Yes
Elementary:
Intermediate:
Specialized:
The objective of this course is to teach the student the theoretical and
Objective
simulation control applications of artificial neural network on mechanical
systems such as robotics, mechanisms.
Types of Neural Networks, training algorithms, modeling dynamic systems
Content
with neural network, practical applications of neural networks.
The emphasis of this course will be on the theoretical aspects of neural
Methodology
network techniques. These techniques will involve design of neural model of
mechanical systems and neural controller for robotics systems. This will be a
lecture course with 3 hours of lecture per week to be arranged.
Only graduate students are accepted for the course
Requirements
Midterm Exam (40%) Final Exam (60%)
Assessment
Yıldırım S. 1999. Neural network Lecture Notes. Kayseri.
Recommended
Matlab Software 2009.
Materials
Course Outline
Neuro computer hardware.
1st week
Neural programming.
2nd week
Neural network models.
3rd week
Dynamic backpropagation algorithm, quickpropagation algorithm.
4th week
Kohenen’s algorithm.
5th week
Machine learning for optimal self organization.
6th week
Neural network applications.
7th week
8th week
MID-TERM EXAM
Neural networks types for controlling mechanical systems.
9th week
Introduction Matlab Neural Network Tool Box.
10th week
Feedforward neural controller, recurrent neural controller.
11th week
Simulation applications for controlling systems.
12th week
Lab. Applications.
13th week
14th week
FINAL EXAM