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EE7207 - Neural & Fuzzy Systems

Learning Objective:

This course is intended to provide students with an in depth understanding of the fundamental theories and learning methods, as well as advanced issues of neural networks and fuzzy logic systems. After the course, the students will be able to apply the learned knowledge to solve problems in their respective research fields.

Content:

Introduction to artificial neural networks. Recurrent and Hopfield Neural Network. Multi-layer perceptron. Radial basis function. Support vector machines. Self-organizing map. Applications of neural network. Fundamentals of fuzzy logic and fuzzy systems. Takagi-Sugeno (T-S) fuzzy modeling and identification. Stability analysis of fuzzy systems. Applications of fuzzy systems.

Learning Outcome:

• Gain an in depth understanding of fundament theories, learning methods and advanced issues of neural network and fuzzy logic.
• Be able to apply the learned knowledge of neural and fuzzy systems to solve their research problems.

Textbooks:

• S. Haykin, "Neural Networks and Learning Machines, 3rd Edition," Prentice Hall, 2009.
• G. Feng, "Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach," CRC Press Inc, 2010.

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