This course introduces students to the fundamentals of three important techniques of artificial intelligence (AI), namely, artificial neural networks (ANN), genetic algorithm (GA), and fuzzy logic. These techniques have been successfully applied by many industries in consumer products and industrial systems. ANN provides strong generalization and discriminant properties and offer a simple way of developing system models and function approximation. GA is adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetics for optimization and search problems. Fuzzy logic offers flexibility in developing rule-based systems using natural language type of rules. They are highly applicable for many pattern recognition applications. This course gives the students appropriate knowledge and skills to develop, design and analyze effectively these AI techniques for practical problems with some degree of accuracy. The students will also be given a hands-on programming experience in developing fuzzy logic and neural networks system as well as genetic algorithm, to effectively solve real world problems. 1. Design systems using ANN, GA and fuzzy logic for real world applications based on theoretical framework. 2. Demonstrate the ability to acquire information from various resources about the development of ANN, GA and fuzzy logic for various applications. 3. Demonstrate the ability to develop fuzzy logic, ANN and/or GA using appropriate programming languages or software tools for solving application.
Malaysia-Japan International Institute of Technology UTMKL Lecture and Discussion, Co-operative and Collaborative Method, Problem Based Method. week 1, week 2, etc. Prof. Datin Dr. Rubiyah Yusof conditional SMJE 3203 3 Sem 6 1. J. McCarthy, What is Artificial Intelligence http://www-formal.stanford.edu/jmc/whatisai/whatisai.html 2. S. N. Sivanandam, S. Sumathi and S. N. Deepa, Introduction to Fuzzy Logic using MATLAB, Springer-Verlag, Berlin, 2007 3. Fuzzy Logic Toolbox For Use with MATLAB® , The Mathworks Inc., 2006 4. Neural Network Toolbox For Use with MATLAB® , The Mathworks Inc., 200 Test, Assignment, Project, Final Examination Prof. Datin Dr. Rubiyah Yusof
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