A defuzzification-free hierarchical fuzzy system (DF-HFS): Rock mass rating prediction

Mutlu, Begüm; Sezer, Ebru Akçapınar; Nefeslioğlu, Hakan A.

doi:10.1016/j.fss.2016.01.001

Cited by 31 publications

(9 citation statements)

References 28 publications

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“…We have attempted to verify the results of our method in comparison to datadriven methods in the case when the number of antecedents in reasoning rules is too large for them to be covered by experts, but there is a possibility of "structuring" of the knowledge base (Mutlu et al 2017). As a test case we used the so-called "Adults" data set (Kohavi and Becker 1996).…”

Section: Examplementioning

confidence: 99%

“…In the last decades reasoning systems based on declarative knowledge acquired from observations (a data-driven approach) are opposed to those in which knowledge is formulated by experts (a knowledge-based approach) (Mutlu et al 2017). It is generally accepted that the data-driven knowledge acquisition is much more effective (Hüllermeier 2015).…”

Section: Introductionmentioning

confidence: 99%

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Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

Macioł

2021

Nauka – Technika – Technologia. Tom 2

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The paper describes a solution of a problem of developing of fuzzy rules compliant with the Takagi-Sugeno approach where a number of available examples (observations) is not sufficient. Modeling of fuzzy premises and generating functions describing a dependence of a result variable on antecedents are described. In our original approach a problem of identification of membership functions of variables com-posing premises and a problem of consequent parameters identification are solved. For the first one, we used a simple technique based on individual judgements of experts. The second one is solved with a linear programming method. In particular, our approach to formulate the consequent parameter identification problem allows using of an extremely effective T-S method when a data-driven approach cannot be applied. In the paper we present a description of our methods and results of simulations of accuracy of the proposed approach, based on commonly known benchmarks. The achieved accuracy of classification is sufficient for the most of decision-making systems of an expert nature.

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Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

Macioł

2021

Nauka – Technika – Technologia. Tom 2

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“…İlk kez Zadeh [16] tarafından önerilen bulanık küme kuramı, karmaşık ve tanımlanmasında güçlüklerin yaşandığı sistemlerin modellenmesinde esnek ve tutarlı bir şekilde uygulanabilmektedir [17]. Bulanık küme kuramı mühendislik jeolojisi uygulamalarında birçok araştırmacı tarafından kullanılmıştır [18]- [23]. Ayrıca, değişik zemin sınıflama sistemlerindeki belirsizliklerin azaltılması amacıyla kullanıldığı çalışmalar da mevcuttur.…”

Section: Bulanıklaştırma Süreciunclassified

An application of a fuzzy classification routine suggested for fine grained soils

Toksöz¹,

Yilmaz²

2020

Pamukkale J Eng Sci

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Mühendislik jeolojisinde tasarıma yönelik ön çalışmalarda zeminlerin sınıflandırılması önemli bir aşamadır. Bir zemine atanabilecek birkaç harf ya da sayı uygulayıcı mühendislere çok hızlı olarak bir fikir sağlamaktadır. Ulusal ve uluslararası zemin sınıflama sistemlerinde ince taneli zeminlerin sınıflandırılması plastisite abağı ile yapılmaktadır. Ancak, likit limit ve plastik limit deneylerini etkileyen bazı faktörlere bağlı olarak zemin sınıfının plastisite abağı ile belirlenmesi sırasında belirsizlikler oluşmaktadır. Ayrıca, tanımlama noktalarının kimi zaman sınıfları ayıran çizgiler üzerine veya yakınına düşmesi durumunda belirsizlik seviyesi artmaktadır. Bu çalışmada söz konusu belirsizlikleri en aza indirmek için daha önce geliştirilen bir bulanık sınıflama sürecinin uygulaması yapılmıştır. Söz konusu bulanık sınıflama sürecinde plastisite abağındaki tanımlama noktalarının sınırları ayıran çizgilere olan dik mekansal mesafeleri dikkate alınmıştır. Sigmoid fonk † siyonu kullanılarak tanımlama noktalarının bulanık plastisite ve bulanık zemin değerleri hesaplanmıştır. Son olarak bulanık işlemciler yardımı ile bulanık zemin sınıfları elde edilmiştir. Önerilen bulanık sınıflama süreci 100 adet laboratuvar verisi üzerinde uygulanmış ve elde edilen bulanık zemin sınıfları klasik zemin sınıfları ile karşılaştırılmıştır. Soil classification is an important stage in preliminary studies for design applications in engineering geology. A few letters or numbers assigned to a soil provide rapidly an idea to engineers. The classification of fine grained soils in national and international soil classification systems is determined using plasticity chart. However, because of the factors affecting liquid and plastic limit tests, uncertainties can arise in classification of fine-grained soils when this approach is used. Furthermore, uncertainty level has increased in case points fall sometimes on the lines or near the lines. In this study, an application of a fuzzy classification routine which was suggested before to minimize these uncertainties was performed. In the suggested method, the spatial distances of the evaluation points on the chart from the lines were considered. The membership degrees that define the fuzzified soil and plasticity were determined by using sigmoid function. Finally, fuzzy soil classes were obtained by using fuzzy operators. The suggested fuzzy classification routine, was performed on classification of 100 fine-grained soil samples ad obtained fuzzy soil classes were compared with crisp soil classes.

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“…One of promising fuzzy models recently developed relates to the multilayer fuzzy structure which is improved from hierarchical fuzzy one [14,15] in which the output of the previous fuzzy layer is the input of the following one with the last output is through a fuzzy model. Nowadays the hierarchical fuzzy model is increasingly improved and successfully applied in the field of intelligent identification and control [16][17][18] such as Kien et al [19,20] optimized the multilayer fuzzy logic identify the nonlinear uncertain doublecoupled water tanks system by using cascade training method.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Fuzzy Sliding Mode Control for Uncertain PAM Robot Arm Plant Enhanced with Evolutionary Technique

Ánh¹,

Kien²

2021

IJCIS

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This paper introduces an enhanced fuzzy sliding mode control (EFSMC) algorithm used for controlling the uncertain pneumatic artificial muscle (PAM) robot arm containing external disturbances. The nonlinear features of investigated PAM robot arm system are approximated using fuzzy logic. Moreover, the coefficients of fuzzy model are optimum selected by evolutionary differential eveloution (DE) technique. The new EFSMC algorithm is designed based on the traditional sliding mode controller in which the adaptive fuzzy rule is developed based on the Lyapunov stability theory and is fuzzified with Mandani fuzzy scheme. As a consequent, the closed-loop stability of nonlinear uncertain PAM robot arm system is guaranteed to follow the global asymptotic stability. Experimental results are shown. It is evident that the proposed adaptive fuzzy rule suitable with the EFSMC controller which ensures an outperforming method in comparison with other advanced control approaches.

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A defuzzification-free hierarchical fuzzy system (DF-HFS): Rock mass rating prediction

Cited by 31 publications

References 28 publications

Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

Design of Takagi–Sugeno fuzzy systems by learning from examples in case a number of available data is not sufficient

An application of a fuzzy classification routine suggested for fine grained soils

Hybrid Fuzzy Sliding Mode Control for Uncertain PAM Robot Arm Plant Enhanced with Evolutionary Technique

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