This article analyzes the role of context in hierarchical fuzzy controllers based on the decomposition of the input space. The usual consideration in most hierarchical fuzzy systems is the reduction of dimensionality problems. This article will analyze how to profit from the qualities of context as
Granular computing in the development of fuzzy controllers
β Scribed by Witold Pedrycz; George Vukovich
- Publisher
- John Wiley and Sons
- Year
- 1999
- Tongue
- English
- Weight
- 535 KB
- Volume
- 14
- Category
- Article
- ISSN
- 0884-8173
No coin nor oath required. For personal study only.
β¦ Synopsis
This study elaborates on the role of information granularity in the development of fuzzy controllers. As opposed to numeric data being commonly accepted by fuzzy controllers, we discuss a general processing framework involving data-information granules exhibiting various levels of information granularity. The paper analyzes an impact of information granularity on the performance of the controller. We study a way in which information granules arise in control problems, elaborate on a way of describing these granules as well as provide a way of quantifying the level of information granularity. A number of analysis and design issues are studied including robustness of the fuzzy controller, representation of linguistic information and quantification of its granularity. Nonlinear characteristics of the compiled version of the fuzzy controller operating in presence of granular information are discussed in detail. Illustrative numerical examples are provided as well.
π SIMILAR VOLUMES
In recent years the use of genetic or evolutionary techniques has produced interesting results in the automatic generation of knowledge bases for fuzzy logic controllers. Three different representations of the rule base have been considered: lists of rules, relational matrices, and decision tables.
## Abstract A general framework is developed to control simulation parameters that appear in finite element models in order to improve accuracy and efficiency. This approach is based on fuzzy logic that allows the expert knowledge to be taken into account on the controller design and avoids the req
This article presents a neuralαnetwork-based fuzzy logic control NNαFLC system. The NNαFLC model has the learning capabilities for constructing membership functions and extracting fuzzy rules from training examples. Both unsupervised and supervised training algorithms are used to find the membership