Cause and effect analysis by fuzzy relational equations and a genetic algorithm

Ротштейн, А. П.; Posner, M. J. M.; Rakytyanska, Hanna

doi:10.1016/j.ress.2005.11.041

Cited by 31 publications

(10 citation statements)

References 4 publications

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“…[3] summarized the fuzzy set theory used in system reliability engineering up to the middle of 90th. Further developments of fuzzy set theory applications in the field of reliability engineering are presented in [11,32,37].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy universal generating functions for multi-state system reliability assessment

Ding

Lisnianski

2008

Fuzzy Sets and Systems

155

109

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Section: Introductionmentioning

confidence: 99%

Fuzzy universal generating functions for multi-state system reliability assessment

Ding

Lisnianski

2008

Fuzzy Sets and Systems

155

109

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“…Based on Larsson and Debor (2007) and Rotshteina et al (2006), it can be said that cause is an event that always produces a given result, i.e. X is required for Y to occur.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…This problem is relevant, especially in Management, where there is a constant need to define causal relationships, and it is very difficult to express cause and effect accurately (Rotshteina, Posnera, & Rakytyanskab, 2006). Models representing cause and effect relationships that can be used for predictive purposes are desired in the business world (Elliott, 2003).…”

Section: Introductionmentioning

confidence: 99%

Framework for the generic process of diagnosis in quality problem solving

Sanches

Meireles

Silva

2014

Total Quality Management & Business Excellence

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This study looks at the problem of determining root causes, presenting an artefact (Causal Determinant) composed of procedures and coded algorithms in software for the purpose of identifying the potentially most relevant cause for a given effect. The design science research approach was used to develop, test and prove the utility of the Causal Determinant artefact. A total of 95 cases were studied at 32 small, medium-sized and large companies. An analysis of the perception of these users showed the easy operation and the effectiveness of the Causal Determinant artefact for selecting a root cause easily and coherently within a set of probable causes of the effect in question. The difficulty in finding the root cause of a problem is one of the most important aspects in the field of total quality management (TQM). A technique that can determine root cause with logical security and at low cost is surely welcome. This paper contributes with a conceptual framework for the generic process of diagnosis in quality problem solving. Large, medium-sized and small industries can apply the proposed method as a system of TQM to determine the root cause of a problem. The output of the method also provides a ratio of importance for each causal factor. This article presents a possible and practical solution for situations faced by organisations in the field of TQM.

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“…In [24], a pure expert system using a genetic algorithm [25] as a tool to solve the diagnosis problem was proposed. This paper presents a further development of [24] and [25].…”

Section: Introductionmentioning

confidence: 99%

Diagnosis Problem Solving Using Fuzzy Relations

Ротштейн

Rakytyanska

2008

IEEE Trans. Fuzzy Syst.

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This paper deals with the restoration and the identification of the causes (diagnoses) through the observed effects (symptoms) on the basis of fuzzy relations and Zadeh's compositional rule of inference. We propose an approach for building fuzzy systems of diagnosis, which enables solving fuzzy relational equations together with design and tuning of fuzzy relations on the basis of expert and experimental information. The essence of tuning consists of the selection such membership functions for fuzzy causes and effects, and also fuzzy relations, which minimize the difference between model and experimental results of diagnosis. The genetic algorithm is used for solving the optimization problem. The proposed approach is illustrated by the computer experiment and the real example of diagnosis.Index Terms-Diagnostic expert systems, fuzzy systems, inverse problems, training.

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Cause and effect analysis by fuzzy relational equations and a genetic algorithm

Cited by 31 publications

References 4 publications

Fuzzy universal generating functions for multi-state system reliability assessment

Fuzzy universal generating functions for multi-state system reliability assessment

Framework for the generic process of diagnosis in quality problem solving

Diagnosis Problem Solving Using Fuzzy Relations

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