Titlebook: ;

Working-Memory

定点

Granulation and Nearest Neighborhoods: Rough Set Approach,borhood consists of data that are semantically related to ., and represents an elementary granule (atoms) of the system under consideration. This paper examines “rough set theory” of these elementary granules. Applications to databases, fuzzy sets and pattern recognition are used to illustrate the idea.

SSRIS

字谜游戏

Fuzzy Clustering for Multiple-Model Approaches in System Identification and Control,nonlinear black-box identification. In the latter case, a comparison with an alternative technique is given. It is shown that fuzzy clustering is an effective technique for the decomposition of a complex nonlinear problem into a set of simpler local problems.

ALLEY

Information Granulation in Automated Modeling,et system. The granulation level of the information involved in this process ranges from low-level data analysis techniques to abstract, qualitative observations about the system. This chapter describes a knowledge representation and reasoning framework that allows this process to be automated.

格子架

Efflorescent

值得尊敬

https://doi.org/10.1007/978-1-4842-2881-4ry and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and fuzzy inference-based systems. We also present in this paper a simple and yet novel defuzzification mechanism.

Asseverate

Ledger

Nonlinear Bounded-Error Parameter Estimation Using Interval Computation,dvocated here which can deal with nonlinear models. This is made possible by the use of the tools of interval analysis, combined with a branch-and-bound algorithm. The purpose of the present paper is to show that the approach can be cast into the more general framework of granular computing.