Computing behavior of finite fuzzy machines – Algorithm and its application to reduction and minimization

Peeva, Ketty; Zahariev, Z.

doi:10.1016/j.ins.2008.07.009

Cited by 22 publications

(16 citation statements)

References 25 publications

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“…This is because the method has been applied in a variety of domains such as geochemical, ecological, and mineralogical indicators in environment science, machine behaviors in computer science, marine safety modeling, port security assessment, etc. ( 10,24,26,27 ) Therefore, the approach has been proven useful for acquiring fuzzy conclusions. The process of the min‐max approach is exhibited in Fig.…”

Section: Methodsmentioning

confidence: 99%

A Novel Human Error Probability Assessment Using Fuzzy Modeling

Ung

Shen²

2010

Risk Analysis

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Human error is one of the significant factors contributing to accidents. Traditional human error probability (HEP) studies based on fuzzy number concepts are one of the contributions addressing such a problem. It is particularly useful under circumstances where the lack of data exists. However, the degree of the discriminability of such studies may be questioned when applied under circumstances where experts have adequate information and specific values can be determined in the abscissa of the membership function of linguistic terms, that is, the fuzzy data of each scenario considered are close to each other. In this article, a novel HEP assessment aimed at solving such a difficulty is proposed. Under the framework, the fuzzy data are equipped with linguistic terms and membership values. By establishing a rule base for data combination, followed by the defuzzification and HEP transformation processes, the HEP results can be acquired. The methodology is first examined using a test case consisting of three different scenarios of which the fuzzy data are close to each other. The results generated are compared with the outcomes produced from the traditional fuzzy HEP studies using the same test case. It is concluded that the methodology proposed in this study has a higher degree of the discriminability and is capable of providing more reasonable results. Furthermore, in situations where the lack of data exists, the proposed approach is also capable of providing the range of the HEP results based on different risk viewpoints arbitrarily established as illustrated using a real-world example.

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Section: Methodsmentioning

confidence: 99%

A Novel Human Error Probability Assessment Using Fuzzy Modeling

Ung

Shen²

2010

Risk Analysis

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“…A»X = B (9) where A = {aij)mxn is a matrix with the coefficients of the system (8), B = (fcj)mxi is the right hand side of (8) and X = (xj) i xn is a matrix with all unknown values of (8) Types of Solutions for the a FLSE Definition 9 X° = (x^)"xi with x^ G [0,1] and j = 1,M is called point solution of the system (9) iiA»X° = B holds.…”

Section: Max-min Fuzzy Linear System Of Equationsmentioning

confidence: 99%

“…Computing Behavior Matrix and Minimization of Finite Fuzzy Machines (FFM) [7] [11], [12], [8] Definition 20 A finite fuzzy machine (FFM) is a quadruple ^ = (X, g, 7, ^), where: • X, g, y are nonempty finite sets of input letters, states and output letters, respectively. • ^ is the set of transition-output matrices of ^, that determines its stepwise behavior.…”

Section: Definition 19mentioning

confidence: 99%

Solving Max-min Relational Equations. Software and Applications

Zahariev¹

2008

AIP Conference Proceedings

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Adaptive gain fuzzy sliding mode control for the synchronization of nonlinear chaotic gyros Abstract. Method, algorithm and software for solving max-min relational equations are presented. They are based on an existing algorithm, but with some significant improvements, developed by the author AppUcations with software implementation are also provided.In the further statement 'matrix' is used instead of 'membership matrix' or 'fuzzy matrix', 'vector' is used instead of 'fuzzy vector'. Definition 2 MatricesA = {aij)mxp and B = {bjk)pxn, where p is the number of the columns in the matrix A and the number of the rows in the matrix B, are called conformable.

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“…From a different point of view, fuzzy automata with membership values in a complete residuated lattice were studied by Ignjatović , Ć irić and their coworkers [19][20][21][22]39,41,42,44,86,87]. Fuzzy automata taking membership values in a lattice-ordered monoid were investigated by Li and others [55,56,58,60], fuzzy automata over other types of lattices were the subject of [3,31,57,59,50,51,[68][69][70][71], and automata which generalize fuzzy automata over any type of lattices, as well as weighted automata over semirings, have been studied recently in [16,30,46]. During the decades, fuzzy automata and languages have gained wide field of application, including lexical analysis, description of natural and programming languages, learning systems, control systems, neural networks, knowledge representation, clinical monitoring, pattern recognition, error correction, databases, discrete event systems, and many other areas (cf., e.g., [32,36,49,64,70]).…”

Section: Introductionmentioning

confidence: 99%