COURSE UNIT TITLE

: NEURAL NETWORK APPLICATIONS IN MECHATRONIC SYSTEMS

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
MEC 5009 NEURAL NETWORK APPLICATIONS IN MECHATRONIC SYSTEMS ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

ASSOCIATE PROFESSOR YAVUZ ŞENOL

Offered to

Mechatronics Engineering
M.Sc. Mechatronics Engineering
Mechatronics Engineering

Course Objective

The course aims to provide an introduction about neural Networks (NN) for mechatronic systems. Fundamental knowledge, neural network types and basic robotic applications will be carried out.

Learning Outcomes of the Course Unit

1   The students are expected to learn basics of neural approach and comparison with human brain
2   The students are expected to understand learning mechanisms of neurons.
3   The students are expected to get information in different neural network structures
4   The students are expected to gain basic skills about the application of neural Networks in practical mechatronic systems
5   The students are expected to prepare a technical report about the project proposals.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Introduction to artificial intelligency, fuzzy systems and genetic algorithms
2 Introduction to neurocomputing
3 Fundamental neurocomputing concepts
4 Adaptive linear combiner, Adaline and Madaline, LMS algorithm
5 Data processing and feature extraction
6 Learning task and learning algorithms
7 Multilayer perceptrons, backpropagation
8 Radial basis function neural networks
9 SOM Networks
10 Hopfield Networks
11 Artificial neural networks for mechatronic systems
12 Artificial neural networks for robot application
13 Student Presentations
14 Student Presentations

Recomended or Required Reading

Textbook(s): Neural Networks: A Comprehensive Foundation (2nd Edition), 1998, Simon Haykin, ISBN: 0132733501, Prentice Hall
Neural Networks and Learning Machines, (3rd Edition), 2008, Simon Haykin, ISBN: 0131471392

Planned Learning Activities and Teaching Methods

The course is done through lectures, assignments and projects in the classroom. Students are expected to participate in all activities, make demo and presentation.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 ASG ASSIGNMENT
2 FIN FINAL EXAM
3 FCG FINAL COURSE GRADE ASG * 0.50 + FIN * 0.50
4 RST RESIT
5 FCGR FINAL COURSE GRADE (RESIT) ASG * 0.50 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Final exam:
1. The students are expected to learn basics of neural approach and comparison with human brain
2. The students are expected to understand learning mechanisms of neurons.
3. The students are expected to get information in different neural network structures
4. The students are expected to gain basic skills about the application of neural Networks in practical mechatronic systems

Homework:
1. The students are expected to learn basics of neural approach and comparison with human brain
2. The students are expected to understand learning mechanisms of neurons.
3. The students are expected to get information in different neural network structures
4. The students are expected to gain basic skills about the application of neural Networks in practical mechatronics systems
5. The students are expected to prepare a technical report about the Project proposals.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)


Doç. Dr. Yavuz ŞENOL
Tel: 0232 3017170
e-mail: yavuz.senol@deu.edu.tr

Office Hours

2 hours per week

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 3 42
Preparing presentations 1 50 50
Preparing assignments 1 35 35
Preparation for final exam 1 20 20
Final 1 3 3
TOTAL WORKLOAD (hours) 192

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14PO.15
LO.132
LO.2322
LO.343
LO.44432
LO.53332232

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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