Data compactification and computing with words

Authors:
Witold Pedrycz;Stuart H. Rubin
Affiliations:
Department of Electrical & Computer Engineering, University of Alberta, Edmonton, Canada AB T6R 2G7 and Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland;Space and Naval Warfare Systems Center, San Diego, code 5634, 53560 Hull Street, San Diego, CA 92152-5001, USA
Venue:
Engineering Applications of Artificial Intelligence
Year:
2010

Citing 0
Cited 2

Human-machine learning for intelligent aircraft systems

AIS'11 Proceedings of the Second international conference on Autonomous and intelligent systems
Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining

Knowledge-Based Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

The underlying objective of this study is to show how fuzzy sets (and information granules in general) and grammatical inference play an interdependent role in information granularization and knowledge-based problem characterization. The bottom-up organization of the material starts with a concept and selected techniques of data compactification which involves information granulation and gives rise to higher-order constructs (type-2 fuzzy sets). The detailed algorithmic investigations are provided. In the sequel, we focus on Computing with Words (CW), which in this context is treated as a general paradigm of processing information granules. We elaborate on a role of randomization and offer a detailed example illustrating the essence of the granular constructs along with the grammatical aspects of the processing.