Neural networks are very different from expert systems where human expertise has to be modeled with rules and frames. In neural networks, the physical machine emulates a human brain and can be taught from experience. An expert system is highly specific to a given problem and cannot be retrained. Neural networks do not model human intelligence or aim to solve specific problems. Instead of putting human expertise into programs, neural network designers put intelligence into the hardware in the form of a generalized capacity to learn.
Neural networks can solve entire classes of problems.
The neural network can be easily modified. Neural networks therefore promise a substantial saving in development cost and time. They allow much greater generality and more closely approximate what we consider intelligence. They are especially useful for visual pattern recognition problems. Define and describe fuzzy logic.