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 | 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 | X |
|---|---|
| Major Area Courses | |
| 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 | Gain sufficient knowledge in mathematics, science and computing; be able to use theoretical and applied knowledge in these areas to solve engineering problems related to information systems. | X | ||||
| 2 | To be able to identify, define, formulate and solve complex engineering problems; to be able to select and apply appropriate analysis and modeling methods for this purpose. | |||||
| 3 | Designs a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; applies modern design methods for this purpose. | |||||
| 4 | To be able to develop, select and use modern techniques and tools required for the analysis and solution of complex problems encountered in information systems engineering applications; to be able to use information technologies effectively. | |||||
| 5 | Designs and conducts experiments, collects data, analyzes and interprets results to investigate complex engineering problems or research topics specific to the discipline of information systems engineering. | |||||
| 6 | Can work effectively in disciplinary and multidisciplinary teams; can work individually. | |||||
| 7 | a. Communicates effectively 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. b. Knows at least one foreign language. | |||||
| 8 | To be aware of the necessity of lifelong learning; to be able to access information, to be able to follow developments in science and technology and to be able to renew himself/herself continuously. | |||||
| 9 | a. Acts in accordance with the principles of ethics, gains awareness of professional and ethical responsibility. b. Gains knowledge about the standards used in information systems engineering applications. | |||||
| 10 | a. Gains knowledge about business life practices such as project management, risk management and change management. b. Gains awareness about entrepreneurship and innovation. c. Gains knowledge about sustainable development. | |||||
| 11 | a. To be able to acquire knowledge about the universal and social effects of information systems engineering applications on health, environment and safety and the problems of the era reflected in the field of engineering. b. Gains awareness of the legal consequences of engineering solutions. | |||||
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 | ||