Fuzzy classifier model to know the sustainability of aquatic organisms and to forecast the aqua farmers

Palani, Suryasankar Ramalakshmi; Balasubramaniyan, Kannapiran; Devaraj, D.

doi:10.1007/s11356-020-08489-7

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Simply put, in the light of FFISs, so many applications can be considered in which applying FFIS yields to more satisfactory results than ever before. The fields in which FFIS can prove applicable include fault detection [17], control systems [18], [19], handoff decision algorithms [20], internet of thing [21], risk assessment [22], [23], evaluating the quality of experience [24], decision support systems [25], fuzzy clustering and classification [26], [27], fuzzy image processing [28], fuel cell stack problem [29], educational systems [30], fuzzy modelling [31], psychology [32], emotion categories [33], packet scheduling algorithms [34], multiobjective optimization problem [35], decision-making [36], heuristic algorithms [37], etc.…”

Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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This paper presents a new machinery of compositional rule of inference called fractional fuzzy inference system (FFIS). An FFIS is a fuzzy inference system (FIS) in which consequent parts of a rule base consist of a new type of membership functions called fractional membership functions. Fractional membership functions are characterized using fractional indices. There are two types of fractional indices. Each type can be either constant or dynamic. An FFIS intelligently considers not only the truth degrees of information included in membership functions, but also the volume of the information in the process of making a conclusion. In other words, the volume of information extracted from a membership function depends on the truth degree of information. Concretely, the higher the truth degree, the larger the volume of information that is involved in the process of making a conclusion. It is shown that typical FISs, e.g. Mamdani's or Larsen's FISs, are special cases of FFISs. Specifically, as the fractional indices approach one, the FFIS approaches a typical FIS. In addition, using two theorems proved in this paper, it is demonstrated that, independent of the problem in question, a typical FIS never leads to results which are more satisfactory than those obtained by the FFIS corresponding to the typical FIS provided that a particular set of fractional indices is taken into account. Put another way, it seems sound to expect that applying FFIS always leads to more satisfactory results than applying its corresponding FIS. It is also shown that FFIS grants a special dynamic to FIS which can be also customized according to a new concept called reaction trajectories map (RTM). Particularly, the RTM enables decision makers to select an FFIS more suitable for their purpose. Some more concepts such as the left and right orders of an FFIS and the fracture index are also introduced in this paper.

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Section: Discussionmentioning

confidence: 99%

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Mazandarani

2020

IEEE Access

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“…p-ISSN : 1412-114X e-ISSN : 2580-5649 http://ojs2.pnb.ac.id/index.php/LOGIC A previous study [8] conducted in 2020 was a study that used the fuzzy classifier as a model to determine the sustainability of aquatic animals and plants by assessing the quality of pond water. The output of this study resulted in fairly good classification performance.…”

Section: Introductionmentioning

confidence: 99%

Design and Construction of Crop Suitability Prediction System Using Fuzzy Logic Classifier Method

Gustina

Salamah

Lindawati

2021

LOGIC

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The potential of land in Indonesia which is quite large and has not been used optimally is one of the problems. this study focused on developing fuzzy logic models to predict plants that are suitable for planting on agricultural land to enable the land use more optimal. In conducting this study, there were two stages of implementation, namely hardware design, and software design which included system workflow design using the Fuzzy Logic Classifier method where three input variables were used, namely soil pH, soil temperature, and humidity. The findings of this study are in the form of predictions consisting of eight outputs, namely Unfavorable Land, Spinach, Cayenne Pepper, Beans, Long Beans, Cucumber, Eggplant, and Tomatoo. The prediction results generated were directly displayed on the LCD of the instrument that has been designed.

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Fuzzy classifier model to know the sustainability of aquatic organisms and to forecast the aqua farmers

Cited by 2 publications

References 17 publications

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

Design and Construction of Crop Suitability Prediction System Using Fuzzy Logic Classifier Method

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