Models of linear programming problems with fuzzy constraints are very well known in the current literature. In almost all cases, to solve these problems, linear membership functions are used because they have very good properties and are very easy to manipulate. In some cases, however, because of the knowledge that the decision maker has, such membership functions could be modeled as nonlinear,...

Data mining is most commonly used in attempts to induce association rules from transaction data. Transactions in real-world applications, however, usually consist of quantitative values. This paper thus proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. We present a GA-based framework for finding membership funct...

A clonal selection algorithm (Clonalg) inspires from Clonal Selection Principle used to explain the basic features of an adaptive immune response to an antigenic stimulus. It takes place in various scientific applications and it can be also used to determine the membership functions in a fuzzy system. The aim of the study is to adjust the shape of membership functions and a novice aspect of the...

In this manuscript, we address the problem of the stability of a certain class of SISO discrete-time processes controlled by an adaptive fuzzy controller, by using Lyapunov stability theory. These results were recently obtained for adaptive neural controllers, and are extended here to adaptive fuzzy controllers of Sugeno's type. In order to achieve tracking of a reference signal with this kind ...

In this work, we extend the genetic lateral tuning of membership functions [1] based on the linguistic 2-tuples representation [2], in order to also perform a tuning of the support amplitude of the membership functions. To do so, we present a new symbolic representation which extends the linguistic 2-tuples representation model with a parameter β to represent the amplitude variation of the supp...

The chapter aims to give computational algorithm to solve a multiobjective non-linear programming problem using Neutrosophic geometric programming technique. As the Neutrosophic optimization technique utilizes degrees of truth-membership, falsity-membership and indeterminacy-membership functions, we made a study of correspondence among those membership functions to see its impact on optimizatio...

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a speci ̄c shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a few variables and the membership optimization problem can be reduced to a parameter optimization problem. The parameter optimization ...