scholarly journals Algorithm for Configuring Fuzzy Inference Systems by Reference Points Based on the Average Value

2021 ◽  
Author(s):  
Mikhail Golosovskiy ◽  
Aleksey Bogomolov ◽  
Dmitriy Tobin
Keyword(s):  
Fuzzy Logic ◽  
Statistical Data ◽  
Fuzzy Inference ◽  
Reference Points ◽  
Fuzzy Inference Systems ◽  
Average Value ◽  
Output System ◽  
Input Variables ◽  
Inference Systems

Abstract In the article an algorithm for configuring Sugeno type fuzzy inference systems based on statistical data is proposed. The algorithm uses the principle of operation based on selecting the area around the reference points, finding the average value in the selected areas, and using it to configure the fuzzy logic output system. The work of the algorithm takes place under the conditions of changing the number of functions belonging to input variables and the number of points of statistical data, on the basis of which the models were configured.

FIS/IFIS Modeling in Professional and Collaborative Learning

2015 ◽  
pp. 326-354
Keyword(s):  
Fuzzy Logic ◽  
Collaborative Learning ◽  
Professional Learning ◽  
Fuzzy Inference ◽  
Test Bed ◽  
Fuzzy Inference Systems ◽  
Computer Supported Collaborative Learning ◽  
Intuitionistic Fuzzy ◽  
Intuitionistic Fuzzy Logic ◽  
Inference Systems

In this chapter, the capability of the fuzzy inference systems (FISs) to model and provide evaluations in the educational context is further explored through the merits of the intuitionistic fuzzy inference systems (IFISs). The Intuitionistic Fuzzy Logic enables the capture and expression of uncertainty and hesitancy with an IFIS model, thus it extends the fuzzy logic capabilities. In this chapter, the purpose and function of the FIS/IFIS modeling, when embedded in an instructional design (ID), is further examined from Boulding's systemic perspective. Elaborations of the latter provide a framework for handling the complexity of the above interplay and clarify the aim and the role of the presented modeling approaches. The ID and FIS/IFIS modeling upon experimental data from their materialization in two educational cases in the area of professional learning and computer supported collaborative learning, respectively, serve as the test-bed for the potentiality of the presented explorations.

Investigation of Hierarchical Fuzzy Inference Systems, when Obtaining Integral Estimates of the Analyzed Objects

2021 ◽  
Vol 27 (11) ◽  
pp. 582-591
Author(s):  
A. A. Sorokin ◽  
Keyword(s):  
Fuzzy Inference ◽  
Training Sample ◽  
Strong Increase ◽  
Hierarchical Systems ◽  
Fuzzy Inference Systems ◽  
Machine Learning Methods ◽  
Output Pattern ◽  
Input Variables ◽  
Inference Systems ◽  
Takagi Sugeno

The purpose of this paper is to study the patterns of the formation of output values in hierarchical systems offuzzy inference. Hierarchical fuzzy inference systems (HFIS) are used to aggregate heterogeneous parameters during the assessment of the state of various elements of complex systems. The use of HFIS allows avoiding the "curse" of the dimension associated with a strong increase in the number and complication of the structure of the production rule, which is characteristic of conventional fuzzy inference systems (FIS), which aggregate the results of interaction of different values of input variables in one knowledge base. As part of the research, numerical experiments were carried out to study the features of the formation of output patterns in HFIS, based on FIS using the Mamdani and Takagi-Sugeno algorithms. As a result of the experiment, it was shown that the output values of the studied HFIS tend to be grouped in the region of fixed values, and the output pattern itself acquires a stepwise character. The revealed property allows using HFIS to distribute the objects of the analyzed sample into groups of states. This property can be used to solve problems of distributing objects into groups in conditions when it is difficult to form a training sample for machine learning methods, but at the same time there is knowledge of the expert group about the features of the functioning of the object of research. Additionally, the paper investigates the features of the formation of output patterns depending on the parameters of the membership functions describing the input variables in HFIS, which are based on FIS using the Mamdani algorithm and HFIS, which are based on FIS using the Takagi-Sugeno algorithm.

USE OF NATURAL VEGETABLE ADAPTOGEN IN BAKERY PRODUCTS TECHNOLOGY

2020 ◽  
Author(s):  
Ю.Н. ВОЛОШИН ◽  
М.М. ЖЕМУХОВА ◽  
Е.Ю. ДОРЕНСКАЯ
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Bakery Products ◽  
Fuzzy Inference Systems ◽  
Interactive Mode ◽  
Baked Products ◽  
Inference Systems ◽  
Logic Inference ◽  
Kinetics Of

Исследована кинетика выпечки хлеба с различным содержанием экстракта лимонника китайского. Приведены результаты органолептического анализа качества полученного продукта с последующей количественной оценкой качества продукта с использованием аппарата нечеткой логики. Установлено, что содержание экстракта лимонника в рецептуре в количестве 15 мл практически не влияет на кинетику выпечки. Сенсорный вкусовой анализ качества выпеченных изделий показал, что наилучшими вкусовыми качествами обладает хлеб, выпеченный по рецептуре с содержанием экстракта лимонника в количестве 3 мл. Использование аппарата нечеткой логики в программном комплексе matlab c расширением fuzzy logic toolbox в блоке нечеткого логического вывода позволяет применять интерактивный режим графических средств редактирования и визуализации всех компонентов систем нечеткого вывода для интерактивной оценки интервальных вкусовых качеств хлеба в зависимости от соотношения ингредиентов рецептуры в соответствии с функцией принадлежности. The paper presents materials on the study of the kinetics of baking bread with different contents of Schisandra chinensis extract and organoleptic analysis of the quality of the resulting product, followed by a quantitative assessment of the quality of the product using a fuzzy logic apparatus. It was established that the content of lemongrass extract in the formulation in the amount of 15 ml practically does not affect the kinetics of baking. Sensory taste analysis of the quality of baked products showed that the best tastes are bread baked according to the recipe with the content of Schisandra extract in the amount of 3 ml. Using the fuzzy logic apparatus in the Matlab software package, the Fuzzy Logic Toolbox extension in the fuzzy logic inference unit allows you to use the interactive mode of graphical editing tools and visualization of all components of fuzzy inference systems for interactive evaluation of interval taste qualities of bread depending on the ratio of recipe ingredients in accordance with membership function.

A MODEL OF DISRUPTIVE TECHNOLOGIES DETERMINATION FOR DEFENSE SPHERE

2021 ◽  
Vol 156 (1) ◽  
pp. 65-83
Author(s):  
Artem KUPCHYN ◽  
Vladyslav SOTNYK
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Fuzzy Evaluation ◽  
Linguistic Variables ◽  
Fuzzy Inference Systems ◽  
Disruptive Technologies ◽  
Proposed Model ◽  
Expert’S Opinion ◽  
Inference Systems ◽  
Input Indicator

The paper describes a model determining the list of disruptive technologies in the defense sector based on the apparatus of fuzzy logic. Input linguistic variables are indicators that determine the criticality of technologies. Each input indicator reflects the presence or absence of a certain feature in the evaluated technologies, so a simple linguistic evaluation is used. However, for the most part only objective evaluations are used without the expert’s opinion. The proposed model is presented in the form of seven fuzzy inference systems, which are arranged hierarchically. The method of bibliometric analysis for determining the prospects of technologies is described and improved. The level of technologies criticality is determined as a result of fuzzy evaluation. It is proposed to apply a new principle of technologies selection as disruptive or critical ones.

Imprecise Knowledge and Fuzzy Modeling in Materials Domain

2016 ◽  
pp. 252-266
Author(s):  
Subhas Ganguly ◽  
Shubhabrata Datta
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems ◽  
Case Studies ◽  
Fuzzy Inference ◽  
Sparse Data ◽  
Fuzzy Modeling ◽  
Empirical Models ◽  
Complex Materials ◽  
Fuzzy Inference Systems ◽  
Inference Systems

This chapter highlights the usage of imprecise knowledge of materials systems using fuzzy inference systems. Experts have knowledge of complex materials systems in the imprecise linguistic form. But due to lack of phenomenological relations, material engineers are compelled to depend on empirical models for practical complex systems. This limitation could be overcome to a certain extent through the method of utilizing this imprecise knowledge with the help of fuzzy logic. The case studies presented here have demonstrated that systems with imprecise knowledge but with sparse data could be modeled successfully in this approach.

Imprecise Knowledge and Fuzzy Modeling in Materials Domain

Fuzzy Systems ◽  
2017 ◽  
pp. 170-183
Author(s):  
Subhas Ganguly ◽  
Shubhabrata Datta
Keyword(s):  
Fuzzy Logic ◽  
Complex Systems ◽  
Case Studies ◽  
Fuzzy Inference ◽  
Sparse Data ◽  
Fuzzy Modeling ◽  
Empirical Models ◽  
Complex Materials ◽  
Fuzzy Inference Systems ◽  
Inference Systems

This chapter highlights the usage of imprecise knowledge of materials systems using fuzzy inference systems. Experts have knowledge of complex materials systems in the imprecise linguistic form. But due to lack of phenomenological relations, material engineers are compelled to depend on empirical models for practical complex systems. This limitation could be overcome to a certain extent through the method of utilizing this imprecise knowledge with the help of fuzzy logic. The case studies presented here have demonstrated that systems with imprecise knowledge but with sparse data could be modeled successfully in this approach.

scholarly journals Applications of fuzzy inference systems in mineral industry-an overview

2021 ◽  
Vol 342 ◽  
pp. 05006
Author(s):  
Michael Galetakis ◽  
Anthoula Vasiliou ◽  
Emmanuel Steiakakis ◽  
Athanasia Soultana ◽  
Vassilios Deligiorgis
Keyword(s):  
Fuzzy Logic ◽  
Literature Review ◽  
Recent Literature ◽  
Fuzzy Inference ◽  
Earth Science ◽  
Mineral Resources ◽  
Fuzzy Inference Systems ◽  
Mineral Industry ◽  
New Approach ◽  
Inference Systems

Recent advances in information and artificial intelligence technologies, and more specifically in Fuzzy Logic and Fuzzy Inference Systems (FIS), have provided a new approach in solving many problems related to mineral industry. The aim of the current study is to examine the application of FIS in mineral resources extractive industry by performing a recent literature review (2010-2020) of related studies published in engineering and earth science oriented scientific journals. Firstly the principles of Fuzzy Logic and the operation of FIS are briefly discussed and a descriptive example of a FIS used in mining with bucket wheel excavators is given. Secondly the results from the literature review are presented and the advantages as well as the trends in future development are discussed.

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