Soft Computing (CMPE466) Course Detail

Course Name Course Code Season Lecture Hours Application Hours Lab Hours Credit ECTS
Soft Computing CMPE466 3 0 0 3 5
Pre-requisite Course(s)
Course Language English
Course Type N/A
Course Level Bachelor’s Degree (First Cycle)
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


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 Adequate knowledge in mathematics, science and computing fields; ability to apply theoretical and practical knowledge of these fields in solving engineering problems related to information systems.
2 Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose.
3 Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4 Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in information systems engineering applications; ability to use information technologies effectively.
5 Ability to gather data, analyze and interpret results for the investigation of complex engineering problems or research topics specific to the information systems discipline.
6 Ability to work effectively in inter/inner disciplinary teams; ability to work individually.
7 a. Effective oral and written communication skills in Turkish; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions. b. Knowledge of at least one foreign language; ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8 Recognition of the need for lifelong learning; the ability to access information and follow recent developments in science and technology with continuous self-development.
9 a. Ability to behave according to ethical principles, awareness of professional and ethical responsibility. b. Knowledge of the standards utilized in information systems engineering applications.
10 a. Knowledge on business practices such as project management, risk management and change management. b. Awareness about entrepreneurship, and innovation. c. Knowledge on sustainable development.
11 a. Knowledge of the effects of information systems engineering applications on the universal and social dimensions of health, environment, and safety. b. Awareness of the legal consequences of engineering solutions.
12 An ability to design, develop, operate and manage cost-effective information systems by assembling the most appropriate software and hardware, arranging appropriate personnel, and defining necessary procedures, in order to enable public and private sector organizations to do their jobs more effectively and be more competitive.
13 Skills in finding solutions to business problems using information technologies.

ECTS/Workload Table

Activities Number Duration (Hours) Total Workload
Course Hours (Including Exam Week: 16 x Total Hours) 16 3 48
Special Course Internship
Field Work
Study Hours Out of Class 16 2 32
Presentation/Seminar Prepration
Project 1 10 10
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