Fuzzy SLIC: Fuzzy Simple Linear Iterative Clustering

Iterative solution of fuzzy linear systems

Linear systems have important applications to many branches of science and engineering. In many applications, at least some of the parameters of the system are represented by fuzzy rather than crisp numbers. So it is immensely important to develop numerical procedures that would appropriately treat general fuzzy linear systems (will be denoted by FLS) and solve them. In this paper firstly a gen...

OPTIMIZATION OF FUZZY CLUSTERING CRITERIA BY A HYBRID PSO AND FUZZY C-MEANS CLUSTERING ALGORITHM

This paper presents an efficient hybrid method, namely fuzzy particleswarm optimization (FPSO) and fuzzy c-means (FCM) algorithms, to solve the fuzzyclustering problem, especially for large sizes. When the problem becomes large, theFCM algorithm may result in uneven distribution of data, making it difficult to findan optimal solution in reasonable amount of time. The PSO algorithm does find ago...

Iterative learning fuzzy control

In this paper an iterative learning control design method is depicted, leading to a feedforward controller minimizing tracking error of repetitive trajectories. The approach is extended to the case of a fuzzy controller, where the plant inverse is approximated by a fuzzy system. This provides some extra features, being also suitable to be applied to nonlinear plants. A simple application illust...

Complex fuzzy linear systems

FUZZY INCLUSION LINEAR SYSTEMS

In this manuscript, we introduce  a new class of fuzzy problems, namely ``fuzzy inclusion linear systems" and   propose a fuzzy solution set for it. Then, we present a theoretical discussion about the relationship between  the fuzzy solution set of a  fuzzy inclusion linear system and the algebraic solution of a fuzzy linear system. New necessary and sufficient conditions are derived for obtain...