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 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

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 Gains accumulated knowledge on mathematics, science and mechatronics engineering; develops an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. X
2 Develops ability to differentiate, identify, formulate, and solve complex engineering problems; develops ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. X
3 Develops ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; develops ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions includes economics, environment, sustainability, producibility, ethics, human health, social and political problems.)
4 Gains ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; develops ability to use information and communications technologies effectively. X
5 Develops ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies.
6 Develops ability to work effectively on single disciplinary and multi-disciplinary teams; gains ability for individual work; develops ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines.
7 Develops ability to express creative and original concepts and ideas orally or written effectively, in Turkish and English language.
8 Develops ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; gains consciousness on the necessity of improvement and sustainability as a result of life-long learning; gains ability for monitoring the developments on science and technology; develops awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation.
9 Gains ability to be conscious on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself.
10 Gains 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 Gains 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; develops consciousness on these issues and develops awareness of legal results of engineering solutions.
12 Gains the competence 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 Gains conciousness on the environmental and social responsibility and develops conciousness to be an individual in society. Gains ability to develop and implement projects and asses them with a critical view for their social implications and gains ability to change the related norms if necessary.
14 Gains the competence on developing strategy, policy and application plans on the mechatronics engineering and evaluating the results in the context of quality standarts.

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