Fuzzy Semigroups

ISBN-13: 9783642057069 / Angielski / Miękka / 2010 / 319 str.

John N. Mordeson; Davender S. Malik; Nobuaki Kuroki

Opis

Lotfi Zadeh introduced the notion of a fuzzy subset of a set in 1965. Ris seminal paper has opened up new insights and applications in a wide range of scientific fields. Azriel Rosenfeld used the notion of a fuzzy subset to put forth cornerstone papers in several areas of mathematics, among other discplines. Rosenfeld is the father of fuzzy abstract algebra. Kuroki is re- sponsible for much of fuzzy ideal theory of semigroups. Others who worked on fuzzy semigroup theory, such as Xie, are mentioned in the bibliogra- phy. The purpose of this book is to present an up to date account of fuzzy subsemigroups and fuzzy ideals of a semigroup. We concentrate mainly on theoretical aspects, but we do include applications. The applications are in the areas of fuzzy coding theory, fuzzy finite state machines, and fuzzy languages. An extensive account of fuzzy automata and fuzzy languages is given in 100]. Consequently, we only consider results in these areas that have not appeared in 100] and that pertain to semigroups. In Chapter 1, we review some basic results on fuzzy subsets, semigroups, codes, finite state machines, and languages. The purpose of this chapter is to present basic results that are needed in the remainder of the book. In Chapter 2, we introduce certain fuzzy ideals of a semigroup, namely, fuzzy two-sided ideals, fuzzy bi-ideals, fuzzy interior ideals, fuzzy quasi- ideals, and fuzzy generalized bi-ideals.

Spis treści

1 Introduction.- 1.1 Notation.- 1.2 Relations.- 1.3 Functions.- 1.4 Fuzzy Subsets.- 1.5 Semigroups.- 1.6 Codes.- 1.7 Finite-State Machines.- 1.8 Finite-State Automata.- 1.9 Languages and Grammars.- 1.10 Nondeterministic Finite-State Automata.- 1.11 Relationships Between Languages and Automata.- 2 Fuzzy Ideals.- 2.1 Introduction.- 2.2 Ideals in Semigroups.- 2.3 Fuzzy Ideals in Semigroups.- 2.4 Fuzzy Bi-ideals in Semigroups.- 2.5 Fuzzy Interior Ideals in Semigroups.- 2.6 Fuzzy Quasi-ideals in Semigroups.- 2.7 Fuzzy Generalized Bi-ideals in Semigroups.- 2.8 Fuzzy Ideals Generated by Fuzzy Subsets of Semigroups.- 3 Regular Semigroups.- 3.1 Regular Semigroups.- 3.2 Completely Regular Semigroups.- 3.3 Intra-regular Semigroups.- 3.4 Semisimple Semigroups.- 3.5 On Fuzzy Regular Subsemigroups of a Semigroup.- 3.6 Fuzzy Weakly Regular Subsemigroups.- 3.7 Fuzzy Completely Regular and Weakly Completely Regular Subsemigroups.- 3.8 Weakly Regular Semigroups.- 4 Semilattices of Groups.- 4.1 A Semilattice of Left (Right) Simple Semigroups.- 4.2 A Semilattice of Left (Right) Groups.- 4.3 A Semilattices of Groups.- 4.4 Fuzzy Normal Semigroups.- 4.5 Convexity and Green’s Relations.- 4.6 The Compact Convex Set of Fuzzy Ideals.- 4.7 Fuzzy Ideals and Green’s Relations.- 5 Fuzzy Congruences on Semigroups.- 5.1 Fuzzy Congruences on a Semigroup.- 5.2 Fuzzy Congruences on a Group.- 5.3 Fuzzy Factor Semigroups.- 5.4 Homomorphism Theorems.- 5.5 Idempotent-separating Fuzzy Congruences.- 5.6 Group Fuzzy Congruences.- 5.7 The Lattice of Fuzzy Congruence Relations on a Semigroup.- 5.8 Fuzzy Congruence Pairs of Inverse Semigroups.- 5.9 Fuzzy Rees Congruences on Semigroups.- 5.10 Additional Fuzzy Congruences on Semigroups.- 6 Fuzzy Congruences on T*-pure Semigroups.- 6.1 T*-pure Semigroups.- 6.2 Semilattice Fuzzy Congruences.- 6.3 Group Fuzzy Congruences.- 7 Prime Fuzzy Ideals.- 7.1 Preliminaries.- 7.2 Prime Fuzzy Ideals.- 7.3 Weakly Prime Fuzzy Ideals.- 7.4 Completely Prime and Weakly Completely Prime Fuzzy Ideals.- 7.5 Relationships.- 7.6 Types of Prime Fuzzy Left Ideals.- 7.7 Prime Fuzzy Left Ideals.- 7.8 Fuzzy m-systems and Quasi-prime Fuzzy Left Ideals.- 7.9 Weakly Quasi-prime Fuzzy Left Ideals.- 7.10 Fuzzy Ideals i(f) and I(f).- 7.11 Strongly Semisimple Semigroups.- 7.12 Fuzzy Multiplication Semigroups.- 7.13 Properties of Fuzzy Multiplication Semigroups.- 7.14 Fuzzy Ideal Extensions.- 7.15 Prime Fuzzy Ideals.- 8 Fuzzy Codes on Free Monoids.- 8.1 Fuzzy Codes.- 8.2 Prefix Codes.- 8.3 Maximal Fuzzy Prefix Codes.- 8.4 Algebraic Properties of Fuzzy Prefix Codes on a Free Monoid.- 8.5 Fuzzy Prefix Codes Related to Fuzzy Factor Theorems.- 8.6 Equivalent Depictions of Fuzzy Codes.- 8.7 Fuzzy Codes and Fuzzy Submonoids.- 8.8 An Algorithm of test for Fuzzy Codes.- 8.9 Measure of a Fuzzy Code.- 8.10 Code Theory and Fuzzy Subsemigroups.- 8.11 Construction of Examples by Closure Systems.- 8.12 Examples by *-morphisms.- 9 Generalized State Machines.- 9.1 T-generalized State Machines.- 9.2 T-generalized Transformation Semigroups.- 9.3 Coverings.- 9.4 Direct Products.- 9.5 Decompositions of T-generalized Transformation Semigroups.- 9.6 On Proper Fuzzification of Finite State Machines.- 9.7 Generalized Fuzzy Finite State Machines.- 9.8 Fuzzy Relations and Fuzzy Finite State Machines.- 9.9 Completion of Fuzzy Finite State Machines.- 9.10 Generalized State Machines and Homomorphisms.- 10 Regular Fuzzy Expressions.- 10.1 Regular Fuzzy Expressions.- 10.2 Codes Over Languages.- 10.3 Regulated Codes and Fuzzy Grammars.- References.

O autorze

D.S. Malik is a Professor of Mathematics and Computer Science at Creighton University. He received his Ph.D. from Ohio University in 1985. He has published more than 45 papers and 18 books on abstract algebra, applied mathematics, fuzzy automata theory and languages, fuzzy logic and its applications, programming, data structures, and discrete mathematics.

Blurb

In this book, modeling and control design of electric motors, namely step motors, brushless DC motors, and induction motors, are considered. The book focuses on recent advances on feedback control designs for various types of electric motors, with a slight emphasis on stepper motors. For this purpose, the authors explore modeling of these devices to the extent needed to provide a high-performance controller but at the same time amenable to model-based nonlinear designs. The control designs focus particularly on recent robust adaptive nonlinear controllers to attain high performance. It is shown that the adaptive robust nonlinear controller on its own achieves a reasonably good performance without requiring the exact knowledge of motor parameters. While carefully tuned classical controllers do often achieve required performance in many applications, it is hoped that the advocated robust and adaptive designs will lead to standard universal controllers with minimal need for fine tuning of control parameters.