In this context, FL (Fuzzy Logic) is a problem-solving control system methodology that lends itself to implementation in systems ranging from simple, small, embedded micro-controllers to large, networked, multi-channel PC or workstation-based data acquisition and control systems. It can be implemented in hardware, software, or a combination of both. FL provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy, or missing input information. FL's approach to control problems mimics how a person would make decisions, only much faster. In the case of fuzzy system, we usually assume the input-output pairs have the structure of fuzzy if-then rules that relate linguistic of fuzzy variables whose values are words (fuzzy sets) instead of numbers. Linguistic variables facilitate interpolation by allowing an approximate match between the input and the antecedents of the rules. Generally, fuzzy systems work well when we can use experience or introspection to articulate the fuzzy if-then rules. When we cannot do this, we may need neural-network techniques to generate the rules. Here arise adaptive fuzzy systems.