fuzzy logic system
The use of fuzzy logic is rapidly spreading in the realm of consumer products design in order to satisfy the following requirements: (1) to develop control systems with nonlinear characteristics and decision making systems for controllers, (2) to cope with an increasing number of sensors and exploit the larger quantity of information, (3) to reduce development time, (4) to reduce costs associated with incorporating the technology into the product. Fuzzy technology can satisfy these requirements for the following reasons.
Nonlinear characteristics are realized in fuzzy logic by partitioning the rule space, bu weighting the rules, and by the nonlinear membership function. Rule-based systems compute their output by combining results from different parts of the partition, each part being governed by separate rules. In fuzzy reasoning, the boundaries of these parts overlap, and the local results are combined by weighting them appropriately. That is why the output in a fuzzy system is a smooth, nonlinear function.
In decision-making systems, the target of modeling is not a control surface but the person whose decision-making is to be emulated. This kind of modeling is outside the realm of conventional control theory. Fuzzy reasoning can tackle this easily since it can handle qualitative knowledge (e.g. linguistic terms like “big” and “fast”, and rules of thumb) directly. In most applications to consumer products, fuzzy systems do not directly control the actuators, but determine the parameters to be used for control. For example, they may determine washing time in washing machines, or if it is the hand or the image that is shaking in a camcorder, or they compute which object is supposed to be the focus in an auto-focus system, or they determine the contrast optimal for watching television.
A fuzzy system encodes knowledge at two levels: knowledge which incorporates fuzzy heuristics, and the knowledge that defines the terms being used in the former level. Due to this separation of meaning, it is possible to directly encode linguistic rules and heuristics. This reduces the development time, since the expert’s knowledge can be directly built in.