Defuzzification Function Research Articles

A formal specification for a fuzzy expert system

A fuzzy logic toolkit has been developed for the formal specification language Z. It permits the incorporation of fuzzy concepts into the language while retaining the precision of any Z specification. The toolkit provides the necessary operators, measures and modifiers for the definition and manipulation of fuzzy sets and relations. This paper illustrates how the toolkit can be used to specify a simple fuzzy expert system. The focus is on the specification of the rule base and the operations necessary for fuzzy inferencing. In particular the example illustrates the use of the fuzzy cartesian product and fuzzy set truncation operators and offers a generic definition for a centroid defuzzification function.

Fuzzy Optimal Control in L2 Space

This article presents a framework for studying the existence of optimal control based on fuzzy rules. The framework consists of two propositions: (1) A set of fuzzy membership functions which are selected out of L2 [a,b] is convex and compact metrizable for the weak topology. (2) The defuzzification function is continuous on the set above. Then, the existence of fuzzy optimal control is proved.

An Adaptive Hybrid Fuzzy - Neural Controller

This paper presents investigations toward adaptive fuzzy control using neural networks for the control of nonlinear and time variable physical processes. By using a hybrid structure, the fuzzy logic controller can be composed of its traditional components and two neural networks. The first neural network works as an adaptive rule estimator for online acquisition and modification of the fuzzy rules as it was presented in a previous work. Here a second neural network is proposed as a further development of the hybrid structure which implements the defuzzification function and works as an adaptive neural defuzzifier. The standard one step gradient descent learning procedure is implemented and the output from a conventional feedback controller is used for tuning the parameters of the neural defuzzifier. Comparison with the traditional fuzzy technique is discussed. The results obtained by simulation show the better performance of the proposed hybrid control structure.

Current-mode analogue defuzzifier

A CMOS current-mode approach for implementing the defuzzification function in analogue fuzzy controllers is proposed. The circuit computes the centre of area of the output fuzzy conclusion using switched current adders and a current divider. Simulation results of the defuzzifier show how the circuit can produce a good approximation of the centre of area even if a small number of levels are used to quantise the output variable.