Transformation of a Mamdani FIS to First Order Sugeno FIS

Jassbi, Javad; Alavi, S. H.; Serra, P.; Ribeiro, Rita A.

doi:10.1109/fuzzy.2007.4295331

Cited by 28 publications

(13 citation statements)

References 7 publications

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“…Mamdani & Assilian, 1975;Takagi & Sugeno, 1985), which contains a specific algorithm assigning new, 'fuzzified' values to input variables. Many authors have written on the subject of fuzzy regulators, base rules and linguistic variables (Guney & Sarikaya, 2009;Jassbi et al, 2007;Schlegel, 2002;Talašová, 2003).…”

Section: Methodsmentioning

confidence: 99%

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

et al. 2014

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Due to the suburbanisation process, it is becoming more difficult to properly define rural and urban areas in the Czech Republic. This delimitation problem has been intensively studied in Europe, including the Czech Republic, for decades, but only so-called 'crisp' rules have been set for the categorisation of urban and rural. This is no longer satisfactory because of substantial population movements. Our research focuses on applying fuzzy set theory to the delimitation of rural and urban areas and on the subsequent advanced cartographic visualisation. We used the principles of fuzzy regulation, or fuzzy inference systems, on socio-economic data to show the transitional character of municipalities. The generated Main map is at scale of 1:500,000, whereas secondary maps are at scale of 1:2,500,000. Map visualisation of municipalities in the Czech Republic provides a very unique combination of geographical information science, cartography and modern geo-computational methods. Information perception via a map is an adequate way to analyse geographic information, and the problem of delimiting rural and urban areas can be suitably visualised using these methods.

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Section: Methodsmentioning

confidence: 99%

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

et al. 2014

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“…The main disadvantage of the ANFIS is the fact that it uses the Takagi-Sugeno FIS ( [18]). Takagi-Sugeno FIS have lower interpretability when compared to Mamdani FIS ( [20], [19]). …”

Section: A Anfismentioning

confidence: 95%

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Rodrigues

Moura

Jacinto

et al. 2014

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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This paper presents a novel neuro-fuzzy inference system, called RBFuzzy, capable of knowledge extraction and generation of highly interpretable Mamdani-type fuzzy rules. RBFuzzy is a four layer neuro-fuzzy inference system that takes advantage of the functional behavior of Radial Basis Function (RBF) neurons and their relationship with fuzzy inference systems. Inputs are combined in the RBF neurons to compound the antecedents of fuzzy rules. The fuzzy rules consequents are determined by the third layer neurons where each neuron represents a Mamdani-type fuzzy output variable in the form of a linguistic term. The last layer weights each fuzzy rule and generates the crisp output. An extension of the ant-colony optimization (ACO) algorithm is used to adjust the weights of each rule in order to generate an accurate and interpretable fuzzy rule set. For benchmarking purposes some experiments with classic datasets were carried out to compare our proposal with the EFuNN neuro-fuzzy model. The RBFuzzy was also applied in a real world oil well-log database to model and forecast the Rate of Penetration (ROP) of a drill bit for a given offshore well drilling section. The obtained results show that our model can reach the same level of accuracy with fewer rules when compared to the EFuNN, which facilitates understanding the operation of the system by a human expert.

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“…The TSK method has been shown to be superior with respect to computational time than the rival Mamdani method (Jassbi et al 2006). It is also more robust and accurate in working with noisy data as compared to the Mamdani method (Jassbi et al 2007). Additionally, impact assessment models, which are used to derive the equivalence factors for different emissions, have inherent uncertainties.…”

Section: Characterizing Data Uncertaintymentioning

confidence: 97%

Application of life cycle assessment in the mining industry

Awuah–Offei

Adekpedjou

2010

Int J Life Cycle Assess

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Background, aim, and scope In spite of the increasing application of life cycle assessment (LCA) for engineering evaluation of systems and products, the application of LCA in the mining industry is limited. For example, a search in the Engineering Compendex database using the keywords "life cycle assessment" results in 2,257 results, but only 19 are related to the mining industry. Also, mining companies are increasingly adopting ISO 14001 certified environmental management systems (EMSs). A key requirement of ISO certified EMSs is continual improvement, which can be better managed with life cycle thinking. This paper presents a review of the current application of LCA in the mining industry. It discusses the current application, the issues, and challenges and makes relevant recommendations for new research to improve the current situation. Main features The paper reviews the major published articles in the literature pertaining to LCA methodology as applied in the mining industry. The challenges associated with LCA applications in mining are discussed next. Finally, the authors present recommended research areas to increase the application of LCA in the mining industry. Results The literature review shows a limited number of published mining LCA studies. The paper also shows the variation in functional unit definition for mining LCA studies. The challenges and research needed to address the problems are highlighted in the discussions. Discussion The limited number of mining LCAs may be due to the lack of life cycle thinking in the industry. The paper, however, highlights the major contributions in the literature to LCA practice in the mining industry. This paper discusses the lack of LCA awareness and tools for mining LCAs, issues relating to functional unit and scoping of mining product systems, defining adequate and appropriate impact categories, and challenges with uncertainty and sensitivity analysis. The authors recommend that future research focus on the development of a mining-specific LCA framework, data uncertainty characterization, and software development to increase the application of LCA in mining. Conclusions LCA presents beneficial insights to the mining industry as it seeks to develop world-class EMSs and environmentally sustainable projects. However, to take full advantage of this technique, further research is necessary to improve the level of LCA application in mining. Major challenges have been identified, and recommended research areas have been proposed to improve the situation. The paper outlines the benefits of increased application of LCA in the mining industry to LCA databases and all practitioners. Recommendations and perspectives It is recommended that additional research be undertaken through industry-academia partnerships to develop a more rigorous mining-specific LCA framework. Such a framework should allow for sensitivity and uncertainty analysis while allowing for suitable data collection that still covers the temporal and spatial dimensions of mining. Research should also be carr...

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Transformation of a Mamdani FIS to First Order Sugeno FIS

Cited by 28 publications

References 7 publications

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

Using a fuzzy inference system to delimit rural and urban municipalities in the Czech republic in 2010

Generating interpretable Mamdani-type fuzzy rules using a neuro-fuzzy system based on radial basis functions

Application of life cycle assessment in the mining industry

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