Abstract
Neural networks can be used in several ways to build an expert network. The simplest is by building a fuzzy cognitive map, originally developed by B. Kosko at USC. The term fuzzy cognitive map refers to the use of fuzzy mathematics in building an expert network that can handle the expertise of multiple experts. Fuzzy math is used to deal with situations that are not clear-cut and precise. A fuzzy cognitive map is one of the easiest expert networks to build. It has interesting advantages over the more traditional rule-based systems, particularly, in its ability to deal with multiple experts, even when they disagree. In addition, because no explicit rules need to be articulated, the development time for a fuzzy cognitive map is dramatically less than for a rule-based system. A fuzzy cognitive map sets up a series of nodes, each of which is a fuzzy set. Each node represents a particular concept or object that is relevant to the problem.
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