Bu çalışmada, Erzurum İl sınırları içerisinde belirlenen lokalitelerde 2015-2016 yıllarının Mayıs-Eylül ayları arasında arazi çalışmaları yapılmış ve bitki örnekleri toplanmış, teşhis edilmiş ve herbaryum örnekleri hazırlanmıştır. Toplanan bu bitkilerden tıbbi önemi olanların Türkçe adı, içerdiği bileşikler, etki ve kullanılışı gibi bilgiler çeşitli kaynaklardan araştırılmış, arazide çekilen fotoğrafları ile birlikte paylaşılmıştır. Bu çalışmanın sonucunda tıbbi önemi olan ve Erzurum'un doğal bitki örtüsünde yetişen başlıca Asteraceae, Leguminosae ve Labiatae olmak üzere 25 familyaya ait 49 bitki belirlenmiştir.

This paper proposed Mamdani-based Adaptive Neuro-Fuzzy Inference System (MANFIS). In literature, there are very applications of Sugeno Adaptive Neuro-Fuzzy Inference System (ANFIS) because of simplicity of the Sugeno defuzzification step. Mamdani defuzzification step is linguistic but the Sugeno defuzzification step has constant and linear functions. So, Mamdani parameters training algorithms given in the open literature are not efficient and give worse results when compared to the Sugeno ANFIS. The proposed Mamdani ANFIS is tested for an equation and to predict vehicle soot emission that soot emission is effective at global warming and melting of sea ice in the Arctic. The proposed Mamdani ANFIS is compared to the Sugeno ANFIS for Least Square Estimation method and Gradient Descent method. The training results show that The Mamdani ANFIS consumes less time and needs less epoch number. It is determined that for Gradient Method, the proposed Mamdani has less training error.

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Purpose This study aims to propose, as the first time, the interval type-2 adaptive network-fuzzy inference system (ANFIS) structure, which is given better results compared to previously presented in the open literature. So, the ANFIS can be used effectively for training of interval type-2 fuzzy logic system (IT2FLS) parameters. Design/methodology/approach Karnik–Mendel algorithm (KMA) is modified to use in interval type-2 ANFIS. The modified Karnik–Mendel algorithm (M-KMA) is implemented to change the uncertain ANFIS parameters into known ones. In this way, the interval type-2 ANFIS removes uncertainties of IT2FLS. Therefore, the interval type-2 ANFIS is reduced to a simple one, i.e. less mathematical operation required. Only consequent parameters are trained, and the consequent parameters are chosen in the form of crisp. Findings By applying the mentioned procedure, it can be shown that interval type-2 ANFIS has generally better results compared to type-1 ANFIS. However, it was noticed that the worst results obtained in the case of interval type-2 ANFIS are equal to the best result obtained in the case of type-1 ANFIS. Therefore, users in this field can use this approach in solving nonlinear problems. Practical implications The interval type-2 ANFIS can be used as controller for highly nonlinear systems such as air vehicles. Originality/value As stated in the open literature, it is ineffective to use ANFIS for IT2FLS. In this study, the KMA is modified for IT2FLS, and it is seen that the ANFIS can be used effectively for IT2FLS.