The module on Intelligent Control and Soft Computing focuses on the design and implementation of Artificial Intelligence (AI) methods such as Artificial Neural Networks (ANNs) and Fuzzy Logic Controllers (FLCs). Students gain hands-on experience in MATLAB & SIMULINK, Python, and related environments, enabling them to develop practical skills. The module combines theoretical concepts with practical application development, allowing students to tackle modern engineering problems and get hands-on experiences in implementing AI methods. By the end of the module, students will be able to analyze concepts, develop FLCs and ANNs, implement ANNs for complex problems, and diagnose intelligent systems for optimal performance. The syllabus covers Intelligent Control Systems, Fuzzy Logic Controllers, Fuzzy Sets and Relations, Fuzzy Control, and Neural Networks.
A. Analyse intelligent control concepts and soft computing from an intelligent perspective B. Develop a Fuzzy Logic Controller Systems (FLCs), incorporating advanced methodologies to optimize performance C. Design and implement an Artificial Neural Networks (ANNs), demonstrating proficiency in design and practical implementation D. Diagnose Intelligent Systems (ANNs & FLCs) for optimum performance & stability
Aimed for final-year students, this module implements the Syntegrative Education Philosophy of XJTLU, which calls for "learning by doing" and "industrial integration". In this module, we aim to implement that as follows: - Classes are conducted in Computer labs, which are pre-installed with the needed software for the class. - Students learn concepts by writing code, creating AI applications, and developing new programs. - Gradually building hands-on AI development experience, knowledge, and confidence. - Students would be encouraged to integrate Industrial/Community needs into their work. - Extra credit would be given to those who successfully create solutions to modern Industrial/Community engineering problems .