Authorised by Academic Registrar, April 1996
Objectives At the completion of this subject students should understand the need for computational methods that are tolerant of imprecision, uncertainty and partial truth; know about the basic theory and applications of fuzzy logic, neural networks, genetic algorithms and belief networks; and be able to build decision support tools using neural networks, fuzzy systems and genetic algorithms.
Synopsis This subject addresses the application of computational methods such as fuzzy logic, neural networks, genetic algorithms and belief networks to Intelligent Decision Support (IDSS). There will be a strong emphasis on the development and application of decision support tools using these techniques in business and industry. The following topics will be covered: uncertain knowledge and reasoning, belief networks, fuzzy logic, decision making in fuzzy environments, building applications using fuzzy software, neural networks, developing applications using neural network simulators, genetic algorithms, application of genetic algorithms to optimisation problems; hybrid systems, combining neural networks fuzzy logic and genetic algorithms for better performance. The students will develop prototype IDSS using packaged software incorporating these techniques.
Assessment Practical work: 60% + Unit test: 40%