Fuzzy decision diagrams for the representation, analysis and optimization of rule bases

Strehl, K.; Moraga, C.; Temme, K.-H.; Stankovic, R.S.

doi:10.1109/ismvl.2000.848610

Cited by 9 publications

(3 citation statements)

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“…Various techniques and models have been developed for the partial minimization of these risks, such as belief networks [52,105], fuzzy models [111,130], Dempster-Shafer models [124], decision diagram techniques [130,154] (in which uncertainty is propagated in the form of "don't care" values), and information-theoretical models [2,19,79,125]. Various techniques and models have been developed for the partial minimization of these risks, such as belief networks [52,105], fuzzy models [111,130], Dempster-Shafer models [124], decision diagram techniques [130,154] (in which uncertainty is propagated in the form of "don't care" values), and information-theoretical models [2,19,79,125].…”

Section: Why Do We Need New Metrics?mentioning

confidence: 99%

Introduction to Noise-Resilient Computing

Yanushkevich

Kasai

Tangim

et al. 2013

Synthesis Lectures on Digital Circuits and Systems

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Section: Why Do We Need New Metrics?mentioning

confidence: 99%

Introduction to Noise-Resilient Computing

Yanushkevich

Kasai

Tangim

et al. 2013

Synthesis Lectures on Digital Circuits and Systems

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“…This paper gives an idea how to perform discretization, which can be treated as "soft" partitioning for fuzzy systems. In addition to potential utilization of the discretization method (see Section 3), we envisage some applications of the information measuring technique (see Section 4) on fuzzy decision diagrams (Strehl et al 1999). …”

Section: Potential Applicationsmentioning

confidence: 99%

Artificial Intelligence in Logic Design

Yanushkevich

2004

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“…3) Fuzzy Decision Diagrams: Based on interval decision diagrams, fuzzy decision diagrams (FuDDs) [34] have been developed. FuDDs can be used for representing, analyzing, and optimizing fuzzy rule bases.…”

Section: A Further Applicationsmentioning

confidence: 99%

Interval diagrams for efficient symbolic verification of process networks

Strehl

Thiele

2000

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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In this paper, a representation of multivalued functions called interval decision diagrams (IDDs) is introduced. It is related to similar representations such as binary decision diagrams. Compared to other functional representations with regard to symbolic formal verification approaches, IDDs show some important properties that enable us to verify process networks and related models of computation more adequately than with conventional approaches. Therefore, a new form of transition relation representation called interval mapping diagram (IMD) is introduced.A novel approach to symbolic model checking of process networks is presented. Several drawbacks of traditional strategies are avoided using IDDs and IMDs. The resulting transition relation IMD is very compact, enabling fast image computations. Furthermore, no artificial limitations concerning buffer capacities or equivalent have to be introduced. Additionally, applications concerning scheduling of process networks are feasible. IDDs and IMDs are defined, their properties are described, and computation methods and techniques are given.

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Fuzzy decision diagrams for the representation, analysis and optimization of rule bases

Abstract: Abstract

Cited by 9 publications

References 20 publications

Introduction to Noise-Resilient Computing

Introduction to Noise-Resilient Computing

Artificial Intelligence in Logic Design

Interval diagrams for efficient symbolic verification of process networks

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