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Publikováno v:
FUZZ-IEEE
In this article, we study a fuzzy optimization problem in business and economics. In this problem, a fuzzy price is determined using a linear one degree demand function. The objective is to find the optimal fuzzy revenue, which is derived from the fu
Autor:
Y. K. Bang, C. H. Lee
Publikováno v:
FUZZ-IEEE
One of the most important goals of time series analysis is prediction basing on the analyzed information. But it is not easy to analyze the patterns, regularities and trends of non-stationary and/or chaos time series because their major characteristi
Publikováno v:
FUZZ-IEEE
The compositional rule of inference (CRI) is widely used in approximate reasoning schemes using fuzzy sets. In this work we discuss the suitability of the Bandler-Kohout subproduct for an alternative inference mechanism from the computational point o
Autor:
Hirosato Seki
Publikováno v:
FUZZ-IEEE
This paper addresses equivalence of fuzzy inference methods. It first presents fuzzy inference methods: the product–sum–gravity method, fuzzy singleton-type inference method and single input rule modules connected type fuzzy inference method (SIR
Publikováno v:
FUZZ-IEEE
This paper considers a new approach to cluster validation in linear fuzzy clustering of relational data. Considering the close connection between linear fuzzy clustering and local PCA, the relational clustering model can be regarded as a multi-cluste
Publikováno v:
FUZZ-IEEE
Particle Swarm Optimization with Turbulence (PSOT) is, in this paper, applied to find out fuzzy models to represent dynamic behavior of space systems that lie underneath the space qualification process. In optimization area, each minimal improvement
Autor:
Mahnhoon Lee
Publikováno v:
FUZZ-IEEE
Clustering is an unsupervised learning method that partitions the objects in a given object set into clusters in which objects are similar. Iterative clustering algorithms have been widely applied in a variety of key areas. Those algorithms find clus
Publikováno v:
FUZZ-IEEE
This paper presents a new clustering algorithm, which is based on entropy regularized fuzzy c-lines, can treat data with some errors. First, the tolerance is formulated and introduce into optimization problem of clustering. Next, the problem is solve
Publikováno v:
FUZZ-IEEE
In this paper, we explore the solution of functional equations I(x,T(y,z)) = T(I(x,y),I(x,z)) and I(x,y) = I(N(y),N(x)) satisfied simultaneously, where T is a strict t-norm, I a fuzzy implication and N a strong negation. Under the assumptions of I co
Publikováno v:
FUZZ-IEEE
The Possibilistic C-means (PCM) was proposed to overcome some of the drawbacks associated with the Fuzzy C-means (FCM) such as improved performance for noise data. However, PCM possesses some drawbacks such as sensitivity in the initial parameter val