| Course Description |
|
| Course Name |
: |
Artificial Neural Networks in Electronic Circuit Design |
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| Course Code |
: |
EE-655 |
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| Course Type |
: |
Optional |
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| Level of Course |
: |
Second Cycle |
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| Year of Study |
: |
1 |
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| Course Semester |
: |
Fall (16 Weeks) |
|
| ECTS |
: |
6 |
|
| Name of Lecturer(s) |
: |
Asst.Prof.Dr. MUTLU AVCI
|
|
| Learning Outcomes of the Course |
: |
Comprehends artificial neural networks in electronic circuit design
Understands artificial neural network hardware realizations
Analyzes artificial neural network applicable type problems
Has ability to apply multi layer perceptron artificial neural network
|
|
| Mode of Delivery |
: |
Face-to-Face |
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| Prerequisites and Co-Prerequisites |
: |
None |
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| Recommended Optional Programme Components |
: |
None |
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| Aim(s) of Course |
: |
To have information about the Implementation of artificial neural networks to solve optimization problems of electronic systems and electronic circuit design and introduction to artificial neural network hardware realizations. |
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| Course Contents |
: |
Artificial neural networks (ANN)in computer aided design amd modelling of electronic circuits and elements.ANN structures used for modelling electronic circuits and elements. ANNs’ used in designing RF/microwave elements and circuits. Solving optimization problems encountered in VLSI design using ANN. Introducing circuits used in implementing ANN. |
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| Language of Instruction |
: |
English |
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| Work Place |
: |
Electrical-Electronics Engineering Department Graduate course classroom |
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Course Outline /Schedule (Weekly) Planned Learning Activities |
| Week | Subject | Student's Preliminary Work | Learning Activities and Teaching Methods |
|
1 |
Introduction to artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
2 |
Multi layer perceptron artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
3 |
MOS transistor modelling |
Reading corresponding chapter of reference book |
Lecturing |
|
4 |
MOS transistor modelling with multi layer perceptron artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
5 |
Radial basis function artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
6 |
MOS transistor modelling with radial basis function artificiall neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
7 |
Design of digital and analog integrated circuit blocks using multi layer perceptron neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
8 |
midterm exam |
|
exam |
|
9 |
Design of digital and analog integrated circuit blocks using radial basis function neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
10 |
High frequency MOS transistor models |
Reading corresponding chapter of reference book |
Lecturing |
|
11 |
RF circuits and simulations |
Reading corresponding chapter of reference book |
Lecturing |
|
12 |
Design of RF integrated circuit using radial basis function artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
13 |
Design of RF integrated circuit using radial basis function artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
14 |
Design of RF integrated circuit using general regression artificial neural networks |
Reading corresponding chapter of reference book |
Lecturing |
|
15 |
ANN hardware implementations 1 |
Reading corresponding chapter of reference book |
Lecturing |
|
16/17 |
ANN hardware implementations 2 |
Reading corresponding chapter of reference book |
Lecturing |
|
|
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Required Course Resources |
| Resource Type | Resource Name |
| Recommended Course Material(s) |
 Q. J. Zhang, K. C. Gupta, Neural Networks for RF and Microwave Design, Artech House, 2000.
S. Haykin, Neural Networks- A Comprehensive Foundation, Prentice Hall, 1999.
O. Nelles, Nonlinear System Identification, Springer, 2001
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| |
| Required Course Material(s) | |
|
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| Contribution of the Course to Key Learning Outcomes |
| # | Key Learning Outcome | Contribution* |
|
1 |
Communicates with people in an appropriate language and style. |
1 |
|
2 |
Specializes by furthering his knowledge level at least in one of the basic subfields of electiral-electronic engineering. |
5 |
|
3 |
Grasps the integrity formed by the topics involved in the field of specialization. |
4 |
|
4 |
Grasps and follows the existing literature in the field of specialization. |
5 |
|
5 |
Comprehends the interdisciplinary interaction of his field with other fields. |
4 |
|
6 |
Has the aptitude to pursue theoretical and experimental work. |
2 |
|
7 |
Forms a scientific text by compiling the knowledge obtained from research. |
3 |
|
8 |
Works in a programmed manner within the framework set by the advisor on the thesis topic, in accordance with the logical integrity required by this topic. |
4 |
|
9 |
Performs a literature search in scientific databases; in particular, to scan the databases in an appropriate manner, to list and categorize the listed items. |
5 |
|
10 |
Has English capability at a level adequate to read and understand a scientific text in his field of specialization, written in English. |
3 |
|
11 |
Compiles his/her knowledge in his/her field of specialization. in a presentation format, and presents in a clear and effective way. |
1 |
|
12 |
Writes a computer code aimed at a specific purpose, in general, and related with his/her field of specialization, in particular |
5 |
|
13 |
Pursues research ın new topics based on his/her existing research experıence. |
4 |
|
14 |
Gives guidance in environments where problems related with his/her field need to be solved, and takes initiative. |
3 |
|
15 |
Develops and evaluates projects, policies and processes in his field of specialization. |
4 |
| * Contribution levels are between 0 (not) and 5 (maximum). |
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