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Neural Networks	The concept of fuzzy sets was first outlined in Zadeh's pioneering paper [1]. Zadeh argued that probability was an insufficient form of representation for uncertainty in Artificial Intelligence. It lacked the ability to model human conceptualisations of the real world. The fuzzy logic approach introduced robustness into systems by allowing a certain amount of imprecision to exist. It paved a way for representing human linguistic terms as fuzzy sets, hedges, predicates and quantifiers. Fuzzy Logic has since played an important role in the management of uncertainty, especially in the current major application areas of Expert Systems and other rule based models. During the last three decades the practical results of fuzzy systems have led to a general acceptance within the AI community [2][3]. To determine whether fuzzy logic is applicable to a problem is to say whether an approximate solution is acceptable. Although input may be crisp, the approximation of the outcome is dependent upon the accuracy of the rule set, the inference technique and the membership function. Fuzzy applications are usually those which require alot of human expertise, judgement and intuition and /or are difficult to automate using existing techniques. The wide number of successful applications of fuzzy theory to real world problems has established the field as an important asset in the design and control of a variety of systems [4][5]. Selected Reading [1] Zadeh, L. Fuzzy Sets. Zadeh, L. Information and Control 8, pp 338-353, (1965). [2] Zadeh, L. Knowledge Representation In Fuzzy Logic. In An Introduction To Fuzzy Logic Applications In Intelligent systems, edited by Yager, R and Zadeh, L, Kluwer Academic Publishers (1992). [3] Cox, E. Fuzzy Systems Handbook. AP Professional (1994). [4] Kosko, B. Fuzzy Thinking. Harper Collins (1994). [5] Yager.R & Filev.D. Essentials of Fuzzy Modelling and Control. John Wiley & Sons (1994)
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