Learning fuzzy control with hybrid symbolic, connectionist networks

The author shows, by means of a real-world example of controlling a steam engine, how hybrid symbolic/connectionist learning systems can be employed for automating the design of fuzzy controllers. Deriving the necessary linguistic variables and accompanying membership functions from raw data by use of machine learning is addressed. It is stressed that the viability of such a system is that it not only acts as a fuzzy controller, but also, independent of human intervention, automatically derives acceptable control strategies