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.

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.

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.

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 Study of Hybrid Neurofuzzy Inference System for Forecasting Flood Event Vulnerability in Indonesia

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Sri Supatmi ◽  
Rongtao Hou ◽  
Irfan Dwiguna Sumitra
Keyword(s):  
Fuzzy Inference ◽  
Flood Event ◽  
Membership Functions ◽  
Fuzzy Inference Systems ◽  
Flood Vulnerability ◽  
Inference System ◽  
Forecasting Accuracy ◽  
Proposed Model ◽  
Inference Systems ◽  
Takagi Sugeno

An experimental investigation was conducted to explore the fundamental difference among the Mamdani fuzzy inference system (FIS), Takagi–Sugeno FIS, and the proposed flood forecasting model, known as hybrid neurofuzzy inference system (HN-FIS). The study aims finding which approach gives the best performance for forecasting flood vulnerability. Due to the importance of forecasting flood event vulnerability, the Mamdani FIS, Sugeno FIS, and proposed models are compared using trapezoidal-type membership functions (MFs). The fuzzy inference systems and proposed model were used to predict the data time series from 2008 to 2012 for 31 subdistricts in Bandung, West Java Province, Indonesia. Our research results showed that the proposed model has a flood vulnerability forecasting accuracy of more than 96% with the lowest errors compared to the existing models.

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.

scholarly journals Precision viticulture data analysis using fuzzy inference systems

OENO One ◽  
2007 ◽  
Vol 41 (1) ◽  
pp. 19
Author(s):  
Mathieu Grelier ◽  
Serge Guillaume ◽  
Bruno Tisseyre ◽  
Thibaut Scholasch
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Sugar Content ◽  
Fuzzy Inference Systems ◽  
Management Actions ◽  
Precision Viticulture ◽  
Inference Systems ◽  
Linguistic Fuzzy Rules ◽  
The Relationship

<p style="text-align: justify;"><strong>Aims</strong>: Various types of data are likely to be used in a precision viticulture framework, to adjust management actions according to within field variations. This paper proposes an alternative way of analysis to classical methods.</p><p style="text-align: justify;"><strong>Methods and Results</strong>: Data are analysed using fuzzy logic techniques. The result is a set of linguistic fuzzy rules induced from data. In this paper, the rules are build in order to explain the relationship between vintage quality, reduced to sugar content, and other available variables. The resulting system is proved to be accurate, moreover thanks to fuzzy logic interpretability, the induced rules are analyzed and compared to expert knowledge.</p><p style="text-align: justify;"><strong>Conclusion</strong>: This example highlights the potential of fuzzy logic to deal with precision viticulture datasets.</p><p style="text-align: justify;"><strong>Significance and impact of study</strong>: This is a preliminary work, it has been carried out using a free software available in the internet.</p>

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