Growing fuzzy inference neural system (GFINN) is a fuzzy–neural network model. Its functionality can be expressed in a form of fuzzy if–then rules. The skill of the GFINN model to grow allows it to change its size and structure according to the training data. The resulting structure allows for a simple input features selection — not all input features have to be used in every fuzzy rule. The ne...

in this paper, rst a design is proposed for representing fuzzy polynomials withinput fuzzy and output fuzzy. then, we sketch a constructive proof for existenceof such polynomial which can be fuzzy interpolation polynomial in a set given ofdiscrete points rather than a fuzzy function. finally, to illustrate some numericalexamples are solved.

short-term prediction of traffic flow is central to alleviating congestion and controlling the negative impacts of environmental pollution resulting from vehicle emissions on both inter- and intra-urban highways. the strong need to monitor and control congestion time and costs for metropolis in developing countries has therefore motivated the current study. this paper establishes the applicatio...

Analytical structure for a fuzzy PID controller is introduced by employing two fuzzy sets for each of the three input variables and four fuzzy sets for the output variable. This structure is derived via left and right trapezoidal membership functions for inputs, trapezoidal membership functions for output, algebraic product triangular norm, bounded sum triangular co-norm, Mamdani minimum infere...

This paper presents a novel learning algorithm of fuzzy perceptron neural networks (FPNNs) for classifiers that utilize expert knowledge represented by fuzzy IF-THEN rules as well as numerical data as inputs. The conventional linear perceptron network is extended to a second-order one, which is much more flexible for defining a discriminant function. In order to handle fuzzy numbers in neural n...

In the approximate fuzzy reasoning the covering over of fuzzy rule base input and rule premise of a rule determines the importance of that fuzzy rule and the rule output as well. An axiom system has been created, describing the relationship between the fuzzy rule base system, rule input and rule output. By using distance-based operators a novel reasoning method appears by the compositional rule...

This paper deals with derivation of analytical structures of fuzzy PI controllers consisting of N ≥ 3 number of triangular input fuzzy sets and 2N − 1 number of trapezoidal output fuzzy sets on the universe of discourse of input and output variables, respectively, linear control rules, different T-norms, different T-conorms, different inference methods, and center of area defuzzification method...

Keywords: Fuzzy DEA Intuitionistic fuzzy DEA Optimistic and pessimistic performance Ranking methods Bank branch efficiency a b s t r a c t Intuitionistic fuzzy set (IFS) is an extension of fuzzy set and an approach to define a fuzzy set where available information is not sufficient to define an imprecise concept by means of a conventional fuzzy set. The existing fuzzy DEA (FDEA) models for meas...

Most of the recent research in the design of fuzzy controllers has been centered on automatic techniques using expert’s input-output data. The majority of these techniques rely on the use of Takagi-Sugeno type controllers and fuzzy-neural-network [3-5,11], fuzzy clustering and fuzzy partition approaches [1,2,6,7]. These controllers, however, are not fully-linguistic and the use of neural-networ...