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Course Description Course Name : Fuzzy Logic Course Code : CENG-559 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. UMUTORHAN Learning Outcomes of the Course : Interprets systems using fundamentals of fuzzy set theory Expresses the relationship between fuzzy set theory and probability theory Solves decision problems using fuzzy logic Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : This course covers fuzzy logic topics used in engineering fields. Course Contents : Introduction, constrains describing, least squares optimization, conventional search methods and tabu search, direct search and genetic algorithms, simulated annealing, ant colony optimization and swarm intelligence, levenberg-marquartd algorithm, non-linear programming and support vector machines, entropy based optimization, optimization in cluster analysis, optimization by principle component analysis, Matlab applications and samples. Language of Instruction : English Work Place : Classroom   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 The concept of fuzzy Reading corresponding subject in lecture notes Lecture 2 Fuzzy sets Reading corresponding subject in lecture notes Lecture 3 Fuzzy membership functions Reading corresponding subject in lecture notes Lecture 4 The features of fuzzy sets Reading corresponding subject in lecture notes Lecture 5 Theoretic operations in fuzzy sets Reading corresponding subject in lecture notes Lecture 6 Fuzzy relations Reading corresponding subject in lecture notes Lecture 7 Uncertainty model fuzziness Reading corresponding subject in lecture notes Lecture 8 Midterm exam Preparation for the exam Written Exam 9 Fuzzy rule based systems and fuzzy decision making Reading corresponding subject in lecture notes Lecture 10 Fuzzy system modeling Reading corresponding subject in lecture notes Lecture 11 Fuzzy clustering Reading corresponding subject in lecture notes Lecture 12 Neural network approach to fuzzy inference systems Reading corresponding subject in lecture notes Lecture 13 Matlab FIS and ANFIS applications Reading corresponding subject in lecture notes Applications with MATLAB 14 Matlab FIS and ANFIS applications Reading corresponding subject in lecture notes Applications with MATLAB 15 Project Presentation Preparing a presentation about the given subject Student oral presentations 16/17 Final exam Preparation for the exam Written Exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Fuzzy Logic with Engineering Applications, T.J. Ross, McGraw-Hill Book Company, 1995. Required Course Material(s)  Foundations on Neuro-Fuzzy Systems, D. Nauck, F. Klawonn, R. Kruse, Wiley, Chichester, 1997  Neural Fuzzy Systems: A Neuro-Fuzzy Synergism., Lin, Prentice Hall, 1996.  Fuzzy Sets, Uncertainity, and Information, G.J. Klir and T.A. Folger, Prentice Hall, 1988.  Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence, J.S.R. Jang, C.T. Sun, and E. Mizutani, Prentice Hall, 1996.  Fuzzy Control, K.M. Passino, S.Yurkovich, Addison-Wesley-Longman, 1998.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 50     Homeworks/Projects/Others 2 50 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Reaches wide and deep knowledge through scientific research in the field of computer engineering, evaluates, implements, and comments. 5 2 Describes and uses information hidden in limited or missing data in the field of computer engineering by using scientific methods and integrates it with information from various disciplines. 5 3 Follows new and emerging applications of computer engineering profession, if necessary, examines and learns them 4 4 Develops methods and applies innovative approaches in order to formulate and solve problems in computer engineering. 5 5 Proposes new and/or original ideas and methods in the field of computer engineering in developing innovative solutions for designing systems, components or processes. 4 6 Designs and implements analytical modeling and experimental research and solves the complex situations encountered in this process in the field of Computer Engineering 4 7 works in multi disciplinary teams and takes a leading role and responsibility. 1 8 Learns at least one foreign language at the European Language Portfolio B2 level to communicate orally and written 1 9 Presents his/her research findings systematically and clearly in oral and written forms in national and international meetings. 3 10 Describes social and environmental implications of engineering practice. 1 11 Considers social, scientific and ethical values in collection, interpretation and announcement of data. 3 12 Acquires a comprehensive knowledge about methods and tools of computer engineering and their limitations. 5 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 2 12 24     Mid-term Exams (Written, Oral, etc.) 1 17 17     Final Exam 1 25 25 Total Workload: 150 Total Workload / 25 (h): 6 ECTS Credit: 6