The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for ...
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The purpose of this paper is to provide a path for designing a tool for decision support to ensure the effectiveness of Quality Management System (QMS). For this, we propose a Fuzzy-Neural Networks (FNN) approach for improving the efficiency of such system. The aim of this approach is to classify the objectives for a real-world case study which presents a major problem for controlling the quality levels of its production lines. This approach provided a significant improvement when the testing data are various or complex.
There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to c...
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There are many areas where objects with very complex and sometimes interdependent features are to be classified; similarities and dissimilarities are to be evaluated. This makes a complex decision model difficult to construct effectively. This paper presents a Hierarchical Fuzzy Signatures (HFS) approach for improving the effectiveness and efficiency of quality-management system (QMS). The goal is to classify the objectives for a real-world case study. The latter was chosen because it presents a major problem for controlling the quality levels of its production lines. With the use of this fuzzy signature structure, complex decision models in the quality management field should be able to be constructed more effectively. In fact, this study provides a path for designing a tool for decision support to ensure the effectiveness of a corporate QMS.
This paper presents a decision support tool for the effectiveness of a Quality Management System (QMS) in a company. To develop this tool, a new approach PAHP based on the combination of the Pareto Optimality Concept ...
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In this paper, two approaches, Hierarchical Fuzzy Signature (HFS) and Neuro-Fuzzy Hierarchical Hybrid (NFHH), have been proposed for piloting a Quality Management System (QMS). These approaches have been applied for r...
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In this paper, two approaches, Hierarchical Fuzzy Signature (HFS) and Neuro-Fuzzy Hierarchical Hybrid (NFHH), have been proposed for piloting a Quality Management System (QMS). These approaches have been applied for real company which presents a major problem for controlling the quality level of production. HFS structure has reduced complexity in the number of input and output meta-levels of hierarchy. Also NFHH model has presented better performance in terms of precision and number of parameters without losing the universal approximation property of neural networks (NN) and fuzzy systems.
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