Soft Computing (COMPE474) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Soft Computing COMPE474 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Natural & Applied Sciences Master's Degree
Mode of Delivery Face To Face
Learning and Teaching Strategies Lecture.
Course Coordinator
Course Lecturer(s)
Course Assistants
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;
  • Implement numerical methods in soft computing
  • Explain the fuzzy set theory
  • Apply derivative based and derivative free optimization
  • Discuss the neural networks and supervised and unsupervised learning networks
  • Comprehend neuro fuzzy modeling
  • Demonstrate some applications of computational intelligence
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
15 Review
16 Review


Course Book 1. J. S. R. Jang, C. T. Sun and E. Mizutai, “Neuro-Fuzzy and Soft Computing”, 1997.
Other Sources 2. 1. Timothy J. Ross, “Fuzzy Logic with Engineering Applications”, McGraw-Hill, 1997.
3. 2. Zioluchian Ali, Jamshidi Mo, “Intelligent Control Systems Using Soft Computing Methodologies”, CRC Press, 2001.
4. 3. D. E. Goldberg, “Genetic Algorithms: Search, Optimization and Machine Learning”, Addison Wesley, N.Y., 1989.
5. 4. S. Rajasekaran and G.A.V.Pai, “Neural Networks, Fuzzy Logic and Genetic Algorithms”, PHI, 2003.
6. 5. 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 Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems.
2 An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems.
3 An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.)
4 An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively.
5 An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies.
6 An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines.
7 An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written.
8 An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation.
9 Be conscious on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself.
10 A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project.
11 Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions.
12 Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions.
13 Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism.
14 A competency on developing strategy, policy and application plans on the mechatronics engineering and evaluating the results in the context of qualitative processes.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Special Course Internship
Field Work
Study Hours Out of Class 16 3 48
Presentation/Seminar Prepration
Project 1 10 10
Homework Assignments 4 5 20
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury 1 10 10
Prepration of Final Exams/Final Jury 1 15 15
Total Workload 103