ECTS - Soft Computing
Soft Computing (CMPE466) Course Detail
Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
---|---|---|---|---|---|---|---|
Soft Computing | CMPE466 | Area Elective | 3 | 0 | 0 | 3 | 5 |
Pre-requisite Course(s) |
---|
N/A |
Course Language | English |
---|---|
Course Type | Technical Elective Courses |
Course Level | Bachelor’s Degree (First Cycle) |
Mode of Delivery | Face To Face |
Learning and Teaching Strategies | Lecture. |
Course Lecturer(s) |
|
Course Objectives | The objective of this course is to teach basic neural networks, fuzzy systems, and optimization algorithms concepts and their relations. |
Course Learning Outcomes |
The students who succeeded in this course;
|
Course Content | Biological and artificial neurons, perceptron and multilayer perceptron; ANN models and learning algorithms; fuzzy sets and fuzzy logic; basic fuzzy mathematics; fuzzy operators; fuzzy systems: fuzzifier, knowledge base, inference engine, and various inference mechanisms such as Sugeno, Mamdani, Larsen etc., composition and defuzzifier. |
Weekly Subjects and Releated Preparation Studies
Week | Subjects | Preparation |
---|---|---|
1 | Introduction to Neuro – Fuzzy and Soft Computing | Chapter 1 (main text) |
2 | Fuzzy Sets | Chapter 2 |
3 | Fuzzy Rules and Fuzzy Reasoning | Chapter 3 |
4 | Fuzzy Rules and Fuzzy Reasoning | Chapter 3 |
5 | Fuzzy Inference Systems | Chapter 4 |
6 | Derivative – Based Optimization | Chapter 6 |
7 | Derivative – Free Optimization | Chapter 7 |
8 | Derivative – Free Optimization | Chapter 7 |
9 | Supervised Learning Neural Networks | Chapter 9 |
10 | Unsupervised Learning Neural Networks | Chapter 11 |
11 | Adaptive Neuro – Fuzzy Inference Systems | Chapter 12 |
12 | Adaptive Neuro – Fuzzy Inference Systems | Chapter 12 |
13 | Coactive Neuro – Fuzzy Modeling | Chapter 13 |
14 | Applications | Chapter 19 – 22 |
Sources
Course Book | 1. J. S. R. Jang, C. T. Sun and E. Mizutai, “Neuro-Fuzzy and Soft Computing”, 1997. |
---|---|
Other Sources | 2. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997. |
3. Zioluchian Ali, Jamshidi Mo, “Intelligent Control Systems Using Soft Computing Methodologies”, CRC Press, 2001. | |
4. D. E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989. | |
5. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003. | |
6. L. H. Tsoukalas, R. E. Uhrig, “Fuzzy and Neural Approaches in Engineering”, John Wiley, N. Y., 1997. |
Evaluation System
Requirements | Number | Percentage of Grade |
---|---|---|
Attendance/Participation | - | - |
Laboratory | - | - |
Application | - | - |
Field Work | - | - |
Special Course Internship | - | - |
Quizzes/Studio Critics | - | - |
Homework Assignments | 4 | 20 |
Presentation | - | - |
Project | 1 | 25 |
Report | - | - |
Seminar | - | - |
Midterms Exams/Midterms Jury | 1 | 25 |
Final Exam/Final Jury | 1 | 30 |
Toplam | 7 | 100 |
Percentage of Semester Work | 70 |
---|---|
Percentage of Final Work | 30 |
Total | 100 |
Course Category
Core Courses | |
---|---|
Major Area Courses | X |
Supportive Courses | |
Media and Managment Skills Courses | |
Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
# | Program Qualifications / Competencies | Level of Contribution | ||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | ||
1 | Has adequate knowledge in mathematics, science, and computer engineering-specific subjects; uses theoretical and practical knowledge in these areas to solve complex engineering problems. | X | ||||
2 | Identifies, defines, formulates, and solves complex engineering problems; selects and applies appropriate analysis and modeling methods for this purpose. | X | ||||
3 | Designs a complex system, process, device, or product to meet specific requirements under realistic constraints and conditions; applies modern design methods for this purpose. | X | ||||
4 | Develops, selects, and uses modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; uses information technologies effectively. | X | ||||
5 | Designs experiments, conducts experiments, collects data, analyzes and interprets results for the investigation of complex engineering problems or research topics specific to the discipline of computer engineering. | |||||
6 | Works effectively in disciplinary and multidisciplinary teams; gains the ability to work individually. | |||||
7 | Communicates effectively in Turkish, both orally and in writing; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
8 | Knows at least one foreign language; writes effective reports and understands written reports, prepares design and production reports, makes effective presentations, gives and receives clear and understandable instructions. | |||||
9 | Has awareness of the necessity of lifelong learning; accesses information, follows developments in science and technology, and continuously improves oneself. | |||||
10 | Acts in accordance with ethical principles and has awareness of professional and ethical responsibility. | |||||
11 | Has knowledge about the standards used in computer engineering applications. | |||||
12 | Has knowledge about workplace practices such as project management, risk management, and change management. | |||||
13 | Gains awareness about entrepreneurship and innovation. | |||||
14 | Has knowledge about sustainable development. | |||||
15 | Has knowledge about the health, environmental, and safety impacts of computer engineering applications in universal and societal dimensions and the contemporary issues reflected in the field of engineering. | |||||
16 | Gains awareness of the legal consequences of engineering solutions. | |||||
17 | Analyzes, designs, and expresses numerical computation and digital representation systems. | X | ||||
18 | Uses programming languages and appropriate computer engineering concepts to solve computational problems. | X |
ECTS/Workload Table
Activities | Number | Duration (Hours) | Total Workload |
---|---|---|---|
Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
Laboratory | |||
Application | |||
Special Course Internship | |||
Field Work | |||
Study Hours Out of Class | 16 | 2 | 32 |
Presentation/Seminar Prepration | |||
Project | 1 | 10 | 10 |
Report | |||
Homework Assignments | 4 | 3 | 12 |
Quizzes/Studio Critics | |||
Prepration of Midterm Exams/Midterm Jury | 1 | 10 | 10 |
Prepration of Final Exams/Final Jury | 1 | 15 | 15 |
Total Workload | 127 |