A novel technique of designing application specific defuzzification strategies with neural learning is presented. The proposed neural architecture considered as a universal defuzzification approximator is validated by showing the convergence when approximating several existing defuzzification strategies. The method is successfully tested with fuzzy controlled reverse driving of a model truck. T...

Mamdani Systems are very well known in the area of Fuzzy Control, where they have been, they are, and they will continue to be successfully used. Efforts to linguistically interpret Mamdani Systems as a method for inference in fuzzy logic have faced the difficulty of interpreting the output of such systems before defuzzification, which consists of an aggregation of normally truncated fuzzy sets...

In this paper experiments are described with three linguistic fuzzy models sharing the same monotone rule base and the same membership functions for the two input variables, but applying different membership functions in the output domain. We investigated which inference methods result in a monotonic input-output behaviour. Apart from the conventional Mamdani–Assilian inference method with Cent...

Lots of problems that contain uncertainty arise in this world. Fuzzy logic is one the methods created to analyze systems these uncertainties. In study, author uses Mamdani method or often also known as Min-Max which an application fuzzy method. The purpose study was determine amount tofu production at UD. Eko Jaya Pasuruan by using MinMax (Mamdani) data used number requests and product inventor...

This paper presents a study on inference in a Fuzzy Logic Controller (FLC). Inference is made up of interpretation, implication, combination and defuzzification. These last two steps can be performed in two sequences. An explanation, through approximate reasoning, on how to implement other than the welìknown Mamdani and Larsen implications is given. (Dis)advantages of several combinations of im...

This paper presents a new learning algorithm for the design of Mamdani-type or fully-linguistic fuzzy controllers based on available input-output data. It relies on the use of a previously introduced parametrized defuzzification strategy. The learning scheme is supported by an investigated property of the defuzzification method. In addition, the algorithm is tested by considering a typical non-...

This paper presents a study on inference in a Fuzzy Logic Controller (FLC). Inference is made up of interpretation, implication, combination and defuzzification. These last two steps can be performed in two sequences. An explanation, through approximate reasoning, on how to implement other than the welìknown Mamdani and Larsen implications is given. (Dis)advantages of several combinations of im...

TYPE-2 fuzzy sets (T2 FSs), originally introduced by Zadeh [3], provide additional design degrees of freedom in Mamdani and TSK fuzzy logic systems (FLSs), which can be very useful when such systems are used in situations where lots of uncertainties are present [4]. The implementation of this type-2 FLS involves the operations of fuzzification, inference,and output processing. We focus on ―outp...

Fuzzy control is one of the most important parts of fuzzy theory for which several approaches exist. Mamdani uses $alpha$-cuts and builds the union of the membership functions which is called the aggregated consequence function. The resulting function is the starting point of the defuzzification process. In this article, we define a more natural way to calculate the aggregated consequence funct...

defuzzifier circuit is one of the most important parts of fuzzy logic controllers that determine the output accuracy. the center of gravity method (cog) is one of the most accurate methods that so far been presented for defuzzification. in this paper, a simple algorithm is presented to generate triangular output membership functions in the mamdani method using the multiplier/divider circuit and...