Fuzzy Relational Systems: Foundations and Principles
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | International Federation for Systems Research International Series on Systems Science and Engineering
20 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Since their inception, fuzzy sets and fuzzy logic became popular. The reason is that the very idea of fuzzy sets and fuzzy logic attacks an old tradition in science, namely bivalent (black-or-white, all-or-none) judgement and reasoning and the thus resulting approach to formation of scientific theories and models of reality. The idea of fuzzy logic, briefly speaking, is just the opposite of this tradition: instead of full truth and falsity, our judgment and reasoning also involve intermediate truth values. Application of this idea to various fields has become known under the term fuzzy approach (or graded truth approach). Both practice (many successful engineering applications) and theory (interesting nontrivial contributions and broad interest of mathematicians, logicians, and engineers) have proven the usefulness of fuzzy approach. One of the most successful areas of fuzzy methods is the application of fuzzy relational modeling. Fuzzy relations represent formal means for modeling of rather nontrivial phenomena (reasoning, decision, control, knowledge extraction, systems analysis and design, etc. ) in the presence of a particular kind of indeterminacy called vagueness. Models and methods based on fuzzy relations are often described by logical formulas (or by natural language statements that can be translated into logical formulas). Therefore, in order to approach these models and methods in an appropriate formal way, it is desirable to have a general theory of fuzzy relational systems with basic connections to (formal) language which enables us to describe relationships in these systems |
| Beschreibung: | 1 Online-Ressource (XII, 369 p) |
| ISBN: | 9781461506331 9781461351689 |
| ISSN: | 1574-0463 |
| DOI: | 10.1007/978-1-4615-0633-1 |
Internformat
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| 500 | |a Since their inception, fuzzy sets and fuzzy logic became popular. The reason is that the very idea of fuzzy sets and fuzzy logic attacks an old tradition in science, namely bivalent (black-or-white, all-or-none) judgement and reasoning and the thus resulting approach to formation of scientific theories and models of reality. The idea of fuzzy logic, briefly speaking, is just the opposite of this tradition: instead of full truth and falsity, our judgment and reasoning also involve intermediate truth values. Application of this idea to various fields has become known under the term fuzzy approach (or graded truth approach). Both practice (many successful engineering applications) and theory (interesting nontrivial contributions and broad interest of mathematicians, logicians, and engineers) have proven the usefulness of fuzzy approach. One of the most successful areas of fuzzy methods is the application of fuzzy relational modeling. Fuzzy relations represent formal means for modeling of rather nontrivial phenomena (reasoning, decision, control, knowledge extraction, systems analysis and design, etc. ) in the presence of a particular kind of indeterminacy called vagueness. Models and methods based on fuzzy relations are often described by logical formulas (or by natural language statements that can be translated into logical formulas). Therefore, in order to approach these models and methods in an appropriate formal way, it is desirable to have a general theory of fuzzy relational systems with basic connections to (formal) language which enables us to describe relationships in these systems | ||
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| 650 | 4 | |a Data Structures, Cryptology and Information Theory | |
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Datensatz im Suchindex
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| bvnumber | BV042420851 |
| classification_tum | MAT 000 |
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| dewey-full | 511.3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 511 - General principles of mathematics |
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| dewey-sort | 3511.3 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4615-0633-1 |
| format | Electronic eBook |
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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:07Z |
| institution | BVB |
| isbn | 9781461506331 9781461351689 |
| issn | 1574-0463 |
| language | English |
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| series2 | International Federation for Systems Research International Series on Systems Science and Engineering |
| spelling | Bělohlávek, Radim Verfasser aut Fuzzy Relational Systems Foundations and Principles by Radim Bělohlávek Boston, MA Springer US 2002 1 Online-Ressource (XII, 369 p) txt rdacontent c rdamedia cr rdacarrier International Federation for Systems Research International Series on Systems Science and Engineering 20 1574-0463 Since their inception, fuzzy sets and fuzzy logic became popular. The reason is that the very idea of fuzzy sets and fuzzy logic attacks an old tradition in science, namely bivalent (black-or-white, all-or-none) judgement and reasoning and the thus resulting approach to formation of scientific theories and models of reality. The idea of fuzzy logic, briefly speaking, is just the opposite of this tradition: instead of full truth and falsity, our judgment and reasoning also involve intermediate truth values. Application of this idea to various fields has become known under the term fuzzy approach (or graded truth approach). Both practice (many successful engineering applications) and theory (interesting nontrivial contributions and broad interest of mathematicians, logicians, and engineers) have proven the usefulness of fuzzy approach. One of the most successful areas of fuzzy methods is the application of fuzzy relational modeling. Fuzzy relations represent formal means for modeling of rather nontrivial phenomena (reasoning, decision, control, knowledge extraction, systems analysis and design, etc. ) in the presence of a particular kind of indeterminacy called vagueness. Models and methods based on fuzzy relations are often described by logical formulas (or by natural language statements that can be translated into logical formulas). Therefore, in order to approach these models and methods in an appropriate formal way, it is desirable to have a general theory of fuzzy relational systems with basic connections to (formal) language which enables us to describe relationships in these systems Mathematics Data structures (Computer science) Artificial intelligence Systems theory Logic, Symbolic and mathematical Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Systems Theory, Control Data Structures, Cryptology and Information Theory Künstliche Intelligenz Mathematik Fuzzy-Logik (DE-588)4341284-1 gnd rswk-swf Fuzzy-Logik (DE-588)4341284-1 s 1\p DE-604 International Federation for Systems Research International Series on Systems Science and Engineering 20 (DE-604)BV000016922 20 https://doi.org/10.1007/978-1-4615-0633-1 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Bělohlávek, Radim Fuzzy Relational Systems Foundations and Principles International Federation for Systems Research International Series on Systems Science and Engineering Mathematics Data structures (Computer science) Artificial intelligence Systems theory Logic, Symbolic and mathematical Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Systems Theory, Control Data Structures, Cryptology and Information Theory Künstliche Intelligenz Mathematik Fuzzy-Logik (DE-588)4341284-1 gnd |
| subject_GND | (DE-588)4341284-1 |
| title | Fuzzy Relational Systems Foundations and Principles |
| title_auth | Fuzzy Relational Systems Foundations and Principles |
| title_exact_search | Fuzzy Relational Systems Foundations and Principles |
| title_full | Fuzzy Relational Systems Foundations and Principles by Radim Bělohlávek |
| title_fullStr | Fuzzy Relational Systems Foundations and Principles by Radim Bělohlávek |
| title_full_unstemmed | Fuzzy Relational Systems Foundations and Principles by Radim Bělohlávek |
| title_short | Fuzzy Relational Systems |
| title_sort | fuzzy relational systems foundations and principles |
| title_sub | Foundations and Principles |
| topic | Mathematics Data structures (Computer science) Artificial intelligence Systems theory Logic, Symbolic and mathematical Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Systems Theory, Control Data Structures, Cryptology and Information Theory Künstliche Intelligenz Mathematik Fuzzy-Logik (DE-588)4341284-1 gnd |
| topic_facet | Mathematics Data structures (Computer science) Artificial intelligence Systems theory Logic, Symbolic and mathematical Mathematical Logic and Foundations Artificial Intelligence (incl. Robotics) Systems Theory, Control Data Structures, Cryptology and Information Theory Künstliche Intelligenz Mathematik Fuzzy-Logik |
| url | https://doi.org/10.1007/978-1-4615-0633-1 |
| volume_link | (DE-604)BV000016922 |
| work_keys_str_mv | AT belohlavekradim fuzzyrelationalsystemsfoundationsandprinciples |