chapter

Rough-fuzzy-neurocomputing based on rough mereological calculus of granules

Authors:

Lech Polkowski,

Maria Semeniuk-PolkowskaAuthors Info & Claims

Design and application of hybrid intelligent systems

January 2003

Pages 623 - 630

Published: 01 January 2003 Publication History

Abstract

In this paper, we propese a scheme for merging rough, fuzzy, and cognitive (neural) computation methods. The underlying paradigm is that of rough mereology. Rough inclusions that are Iogical connectives of rough mereology allow for constructing granules of knowledge that constitute elementary objects for calculi merging rough, fuzzy and neural schemes.

References

[1]

{1} S. Greco, B. Matarazzo, R. Stowinski, and J. Stefanowski, An algorithm for induction of decision rules consistent with the dominance principle. Lecture Notes in Artificial Intelligence2005, Springer, 2001, pp. 304-313.]]

Digital Library

Google Scholar

[2]

{2} S. Lesniewski, On the foundations of mathematics. Topoi2, 1982, pp. 7-52.]]

Google Scholar

[3]

{3} Z. Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer, 1991.]]

Digital Library

Google Scholar

[4]

{4} Z. Pawlak, Rough sets.Intern. J. Comp. Inf Sci. 11, 1982, pp. 341-356.]]

Crossref

Google Scholar

[5]

{5} L. Polkowski, A rough-neural computation model based on approximate calculus of parts (rough mereology), in: S. K. Pal, L. Polkowski, A. Skowron (eds.), Rough-neurocomputing, Springer, 2003 (in print).]]

Digital Library

Google Scholar

[6]

{6} L. Polkowski, Rough mereology: A rough set paradigm for unifying rough set theory and fuzzy set theory. Fundamenta Informaticae54: 1, 2003, pp. 67-88.]]

Digital Library

Google Scholar

[7]

{7} L. Polkowski, A. Skowron, Rough mereology: a new paradigm for approximate reasoning. International Journal of Approximate Reasoning15: 4, 1997, pp. 333-365.]]

Google Scholar

[8]

{8} L. A. Zadeh, J. Kacprzyk (eds.), Computing with Words in Information/Intelligent Systems vol. 1 Physica, 1999.]]

Google Scholar

Index Terms

Rough-fuzzy-neurocomputing based on rough mereological calculus of granules
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Probabilistic reasoning
      2. Vagueness and fuzzy logic
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

Recommendations

Rough-fuzzy-neurocomputing based on rough mereological calculus of granules
Hybrid Intelligence using rough sets

In this paper, we propose a scheme for merging rough, fuzzy, and cognitive (neural) computation methods. The underlying paradigm is that of rough mereology. Rough inclusions that are logical connectives of rough mereology allow for constructing granules ...
Soft rough fuzzy sets and soft fuzzy rough sets

Fuzzy set theory, soft set theory and rough set theory are mathematical tools for dealing with uncertainties and are closely related. Feng et al. introduced the notions of rough soft set, soft rough set and soft rough fuzzy set by combining fuzzy set, ...
Covering-based fuzzy rough sets
The fuzzy system and its application in East Asia

Many researchers have combined rough set theory and fuzzy set theory in order to easily approach problems of imprecision and uncertainty. Covering-based rough sets are one of the important generalizations of classical rough sets. Naturally, covering-based ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

Design and application of hybrid intelligent systems

January 2003

1154 pages

ISBN:1586033948

Editors:
Ajith Abraham
Department of Computer Science, Oklahoma State University
,
Mario Köppen
Department of Pattern Recognition, Fraunhofer IPK-Berlin, Germany
,
Katrin Franke
Department of Pattern Recognition, Fraunhofer IPK-Berlin, Germany

Publisher

IOS Press

Netherlands

Publication History

Published: 01 January 2003

Qualifiers

Chapter

Index Terms

Recommendations