A Comparison of Mandani and Sugeno Inference Systems for a Space Fault Detection Application

Jassbi, Javad; Serra, P.; Ribeiro, Rita A.; Donati, Alessandro

doi:10.1109/wac.2006.376033

Cited by 57 publications

(34 citation statements)

References 3 publications

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“…Further investigation of Jassbi et al confirms that Takagi-Sugeno-Kang method shows more tolerance towards input noise than Mamdani's method [15], which is an advantageous property in the problem domain of the current research. Figure 2 shows that the decrease of the inputs "Processability" and "Code Coverage" below the medium level have a drastic impact on the execution tracing quality which also reflects the expert's opinion.…”

Section: Approach Of Takagi-sugeno-kangmentioning

confidence: 80%

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

Galli

Chiclana

Carter

et al. 2013

APH

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Execution tracing quality is a crucial characteristic which contributes to the overall software product quality though the present quality frameworks neglect this property. In the scope of this pilot study the authors introduce a process to create a model for describing execution tracing as a quality property; moreover, the performance of four different models created is compared. The process and the models presented are capable of capturing subjective uncertainty which is an intrinsic part of the quality measurement process. In addition, the possibility of linking the presented models to software product quality frameworks is also illustrated.

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Section: Approach Of Takagi-sugeno-kangmentioning

confidence: 80%

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

Galli

Chiclana

Carter

et al. 2013

APH

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“…The FAHP approach is used to obtain the relative importance weightage of indicators (see sub-section on importance weightage of indicators and measures). The Mamdani FIS is applied in this study due to its comparatively simpler structure, which predicts reasonable results and also includes the intuitive and interpretable nature of the rule base (Jassbi et al 2006). The FIS consist of four components, namely; fuzzification interface, knowledge base, inference system and defuzzification interface.…”

Section: Sustainability Evaluation Framework and Proposed Methodsmentioning

confidence: 99%

Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework

et al. 2015

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Sustainability has become a necessity, partly due to the threats created by traditional manufacturing practices, and due to regulations imposed by stakeholders. Performance evaluation is an important component of sustainability initiatives in manufacturing organizations. This study proposes a sustainability evaluation method for manufacturing SMEs using integrated fuzzy analytical hierarchal process (FAHP) and fuzzy inference system (FIS) approach. The performance indicators are identified from literature considering the characteristics of SMEs. Balanced scorecard framework is used to categorize the indicators among its four aspects. The linguistic variables are used to collect the opinions of decision makers about the performance ratings and importance of the aspects and corresponding indicators. The FAHP method is applied to determine the relative weights of measures and indicators. The performance ratings of the organization with respect to indicators and relative weights of indicators are combined to obtain the weighted performance ratings. The weighted performance ratings are considered as inputs to FIS. The hierarchal FIS is applied to derive the overall sustainability performance. Using a case study of manufacturing SME, the sustainability score of the organization was elicited in accordance with this procedure. Consequently, a sensitivity analysis of the proposed method reveals the most important basic indicators affecting overall sustainability, identifying areas which decision makers should place special attention. This method can also assist managers of larger enterprises B Ezutah Udoncy Olugu to assess the effectiveness of their sustainability strategies, especially when dealing with suppliers from the SMEs.

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“…In terms of use, the Mamdani FIS is more widely used mostly because of the reasonable results with a relatively simple structure it provides, and the intuitive interpretable nature of the rule base [20]. Since the consequents of the rules in a Sugeno FIS are not fuzzy, this interpretability is lost; however, the Sugeno FIS's rules' consequents can have as many parameters per rule as input values, which results in more degrees of freedom in the design than those of Mamdani and, in turn, provides the system's designer with more flexibility in the design of the system [21].…”

Section: Mamdani Fis Vs Sugeno Fismentioning

confidence: 99%

A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)

Fahmy¹,

Zaher²,

Kandil³

2015

IJCA

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This paper outlines the basic differences between the Fuzzy logic techniques, including Mamdani , Sugeno fuzzy inference system models and Adaptive Neuro-Fuzzy Inference System (ANFIS). The main motivation behind this research is to assess which approach provides the best performance for predicting prices of Fund. Due to the importance of performance in Economy, the Mamdani , Sugeno models and ANFIS are compared with the actual values. Fuzzy inference systems (Mamdani , Sugeno and ANFIS fuzzy models ) can be used to predict the weekly prices of Fund for the Egyptian Market. The application results indicate that (ANFIS) model is better than that of Mamdani and Sugeno . The results of the three fuzzy inference systems (FIS) are compared.

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A Comparison of Mandani and Sugeno Inference Systems for a Space Fault Detection Application

Cited by 57 publications

References 3 publications

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic

Fuzzy-based sustainability evaluation method for manufacturing SMEs using balanced scorecard framework

A Comparison between Fuzzy Inference Systems for Prediction (with Application to Prices of Fund in Egypt)

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