scholarly journals ANALYSIS OF THE EFFECTIVENESS OF TECHNOLOGICAL INNOVATIONS BY FUZZY MODELING METHODS: THE CONTENT OF THE MODEL AND THE CONDITIONS FOR SOFTWARE IMPLEMENTATION

2019 ◽  
Vol 29 (5) ◽  
pp. 609-620
Author(s):  
O.M. Shatalova
Keyword(s):  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Technological Development ◽  
Innovation Management ◽  
Fuzzy Model ◽  
Software Implementation ◽  
Technological Innovations ◽  
Fuzzy Evaluation ◽  
Innovative Strategy ◽  
Intelligent Methods

The article is devoted to the development of methods and instruments for solving the problem of non-stochastic uncertainty in the management of technological innovations. In this regard, the methodology of fuzzy-multiple modeling of systems is considered. It allows you to take into account both deterministic and stochastic data, as well as mental knowledge of the system on the part of decision makers, presented in the lexical description and based on fuzzy evaluation mechanism. The construction of fuzzy-multiple models is aimed at reproducing the logic of decision making and is based on the use of intelligent methods of information processing, including those presented in fuzzy and verbal characteristics, by mathematical language means, which can be transferred to machine processing. The basis of the study is the provision on the vector form of the efficiency indicator and the implementation of the correspondence function between the basic parameters of efficiency through fuzzy inference. The article describes the developed basic conditions for simulation of fuzzy-multiple modeling in assessing the effectiveness of technological innovation management systems - the structure of the model and methods for its construction; presents the means of software implementation of a simulation fuzzy-plural model developed in accordance with these conditions and the results of its practical testing. The developed conditions of fuzzy-multiple modeling in assessing the effectiveness of technological innovations form the basis of a comprehensive analysis of the conditions of technological development of the enterprise, allow to identify significant management factors and form the content of an effective innovative strategy for the technological development of the enterprise; the fuzzy model itself can be considered as a platform for the integration of deterministic, stochastic, expert knowledge of the system.

scholarly journals Risk significance assessment on the basis of a fuzzy model

2021 ◽  
Vol 273 ◽  
pp. 08037
Author(s):  
Inna Nurutdinova ◽  
Luibov Dimitrova
Keyword(s):  
Risk Management ◽  
Expert System ◽  
Fuzzy Inference ◽  
Expert Knowledge ◽  
Fuzzy Model ◽  
Subject Area ◽  
Fuzzy Modelling ◽  
Risk Management Process ◽  
Nature Of Information ◽  
The Subject

The paper considers the problem of risk significance assessment in the activity of a testing laboratory. The accreditation criteria of such laboratories include requirements for implementation of risk management in the organization of their activity. To optimize the risk management process, it is proposed to create an expert system based on fuzzy modelling of the derivation of the risk significance assessment. The expediency of this approach is justified by the expert nature of information on the parameters of risks, subjectivity and uncertainty in their assessments. A description of the subject area “Risk significance assessment” is given, groups of input features and an output parameter are established, the corresponding linguistic variables are introduced, basic and extended term-sets are defined, membership functions are constructed. A fuzzy expert knowledge base has been created, the fuzzy inference of assessing the significance of risks is based on. The proposed algorithm for assessing the significance of risk was tested, confirming its suitability and effectiveness for creating an expert system.

scholarly journals Numerical Implementation of a Fuzzy Model for Evaluating the Effectiveness of Technological Innovations in the Formation of a Portfolio of Projects in an Industrial Enterprise

2019 ◽  
Vol 16 (4) ◽  
pp. 145
Author(s):  
O M Shatalova
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Fuzzy Model ◽  
Industrial Enterprise ◽  
Decision Makers ◽  
Information Support ◽  
Management Decision ◽  
Technological Innovations ◽  
Numerical Implementation ◽  
Management Decision Making

A high uncertainty and fuzziness of information in the systems of development and organizations production of technological innovations are forming the need to use adequate management decision-making tools. A key condition for decisions in such systems is, as a rule, the criterion of efficiency. The paper presents the results of the numerical implementation of a methodological approach to evaluating the effectiveness of technological innovations from the standpoint of non-stochastic uncertainty. The approach is based on fuzzy calculations and fuzzy modeling. This approach is aimed at integrating deterministic, stochastic and expert knowledge of the system; it provides an expanded view of the content of the “effectiveness” category itself and the composition of relevant factors, allows you to take into account the restrictions and preferences of decision makers relevant to the system under study. The considered approach is adapted to the specifics of managing technological innovation in an industrial enterprise. The results of the development and numerical implementation of a fuzzy model for evaluating the effectiveness of technological innovations have led to the conclusion that the approach used expands (complements) the composition of the existing methods in this field of knowledge; the numerical value of the efficiency (W) obtained in the fuzzy model can be considered as an additional analytical indicator of information support of the management decision-making process; the significance of this indicator is due to the fact that the indicator W reveals strategically significant prerequisites and provides clarification (justification) of the values of the key technical and economic parameters. The fuzzy W evaluation model allows for combining deterministic and stochastic data with expert estimates and to formalize mental judgments of decision makers using language means of mathematics. Thus, the prerequisites for building an intelligent automated decision-making system in the management of innovative processes and projects in the enterprise are provided.

Management of sustainable innovation for national development

2018 ◽  
Vol 2 (2) ◽  
pp. 21
Author(s):  
Alvaro Cristian Sánchez Mercado
Keyword(s):  
Civil Society ◽  
Technological Development ◽  
Innovation Management ◽  
Science And Technology ◽  
National Development ◽  
Main Research ◽  
The World ◽  
End Use ◽  
Research Centres ◽  
Scientific And Technological Development

Throughout history the development of the countries has been generated mainly by the impulse in two complementary axes: Science and Technology, and Trade. At present we are experiencing an exponential scientific and technological development and the Economy in all its fronts is driven by the intensive application of technology. According to these considerations, this research tries to expose the development of Innovation Management as a transversal mechanism to promote the different socioeconomic areas and especially those supported by engineering. To this end, use will be made of Technology Watch in order to identify the advances of the main research centres related to innovation in the world. Next, there will be an evaluation of the main models of Innovation Management and related methodologies that expose some of the existing Innovation Observatories in the world to finally make a proposal for Innovation Management applicable to the reality of Peru, so that it can be taken into consideration by stakeholders (Government, Academy, Business and Civil Society) committed to Innovation Management in the country

Determining the safety culture in a gun factory in Turkey: A fuzzy approach

2020 ◽  
pp. 1-11
Author(s):  
Gökçen A. Çiftçioğlu ◽  
Mehmet A. N. Kadırgan ◽  
Ahmet Eşiyok
Keyword(s):  
Safety Culture ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Self Awareness ◽  
Inference System ◽  
Five Factors ◽  
Initial Questionnaire ◽  
Proposed Model ◽  
Assessment Questionnaire

Safety culture is a very complex phenomenon due to its intangible nature. It is tough to measure and express it with numerical values, as there is no simple indicator to measure it. This paper presents a fuzzy inference system that measures the safety culture. First of all, a safety culture assessment questionnaire is developed by utilizing related literature. The initial questionnaire had 29 items. The questionnaire is applied to 259 employees within the gun manufacturing factory. After making an exploratory factor analysis, the questionnaire is based on five factors with 25 items. The safety culture indicators are defined as; safety follow-up audit reporting, employees’ self-awareness, operational safety commitment, management’s safety commitment, safety orientedness. Normality, reliability, and correlation analysis are performed. Then a fuzzy model is constructed with five inputs and one output. The inputs are the five factors mentioned above, and the output generated is the safety culture result, which is between 0-1. The presented fuzzy model produces reliable results indicating the safety culture level from the employees’ eyes. Beyond exploring the employees’ safety culture, the proposed model can easily be understood by the practitioners from various sectors. Furthermore, the model is straightforward to customize for various fields of industry.

Studying Fitting Effect by ANFIS in the Electrodialysis Process

2011 ◽  
Vol 268-270 ◽  
pp. 336-339
Author(s):  
Guo Lin Jing ◽  
Wen Ting Du ◽  
Quan Zhou ◽  
Song Tao Li
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Back Propagation ◽  
Fuzzy Model ◽  
Least Square Method ◽  
Least Square ◽  
Back Propagation Algorithm ◽  
Inference System ◽  
Error Back Propagation ◽  
Error Back Propagation Algorithm

Fuzzy system is known to predict model in the electrodialysis process. This paper aimed to study fitting effect by ANFIS in a laboratory scale ED cell. Separation percent of NaCl solution is mainly as a function of concentration, temperature, flow rate and voltage. Besides, ANFIS(Adaptive Neuro-Fuzzy Inference System) based on Sugeno fuzzy model, its structure was similar to neural network and could generate fuzzy rules automatically, using the error back propagation algorithm and least square method to adjust the parameters of fuzzy inference system. We obtained fitted values of separation percent by ANFIS. Separation percent from experiments compared with the fitted values of separation percent. The result is shown that the correlation coefficient is 0.988. Therefore, it is verified as a good performance in the electrodialysis process.

SUBSTANTIATION OF THE CONCEPT OF REFORMING THE SYSTEM OF ORGANIZATION OF SCIENTIFIC AND TECHNOLOGICAL DEVELOPMENT IN INDUSTRY

2021 ◽  
pp. 91-109
Author(s):  
Olga V. Krasnyanskaya ◽  
Keyword(s):  
Technological Development ◽  
Basic Research ◽  
Specific Weight ◽  
Academic Science ◽  
Technological Innovations ◽  
Total Production ◽  
Organization And Management ◽  
Development Costs ◽  
Production Organization ◽  
Scientific And Technological Development

Despite the fact that Russia remained among the ten leading countries by the share of costs for technological innovations in the total volume of delivered products, the gap in the level of innovation performance remained serious. In terms of the specific weight of technological innovation costs in the total volume of the delivered goods, performed works and rendered services (2,1%), Russia in 2018 ranked 9 th among European countries. In terms of its share of the research and development costs in the total cost of technological innovations (45,2%) – 14th place and in terms of the specific weight of the innovative goods, works, services in the total volume of goods delivered, works performed, services rendered (6,5%) – 24th place out of 30. An analysis of the foreign and Russian experience of the innovation organization showed that in order to multiply the share of innovative industrial products in the total production, it was necessary to create such a system of organization and management of scientific and tech- nological development, which, unlike current practice and by analogy with the key principle of modern concepts of the production organization – the principle of “pulling” – would be able to form a real paid demand for applied R & D and subsequently for basic research. At the same time, in addition to the existing stream of ideas from academic science to production, it is necessary to organize a back-stream of demand – from the factory science, which is at the forefront of understanding the current needs of industry – to the applied one and then to academic science within the range of issues really essential for production.

scholarly journals Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

2020 ◽  
Author(s):  
Adel Bakhshipour ◽  
Hemad Zareiforoush
Keyword(s):  
Fuzzy Logic ◽  
Feature Selection ◽  
Decision Trees ◽  
Fuzzy Inference ◽  
Fuzzy Model ◽  
Principal Component ◽  
Image Features ◽  
Fuzzy Classifier ◽  
Inference System ◽  
Peanut Plant

Abstract A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

scholarly journals DIGI-INNO Tech: Reinventing Innovations

2020 ◽  
pp. 356-360
Keyword(s):  
High Speed ◽  
Social Innovation ◽  
Innovation Management ◽  
Innovation Process ◽  
Value Chains ◽  
Technological Innovations ◽  
Green Innovation ◽  
Digital Innovation ◽  
New Ideas ◽  
Very High

Digital-Innovation Technology calls for reinvention of innovations that offers new opportunities and challenges to design new products and services in the era of hi-tech competition. Digitalization and innovations are pressing issues for business in almost each and every industry. The scope to create new digital value chains increases at a very high speed due to interconnection of people and systems . It is to be believed that wonderful new ideas can open up new ways of looking at various Social Problems because of Digi-Inno connection between people and software. However creating digitalized product and services often creates new problems and challenges to the firm that are trying to innovate. The concept of reinvention in innovation process is redesigning the innovations coupled with advances in science and technology. Technological innovations are only one of many kinds of innovation that develops variety of terms like social innovation, sustainable innovation, responsible and green innovation. In this paper, we tried to give special emphasis on issues of digital innovation management which helps to seek a better base for reinventing innovation management research in digital innovative world.

scholarly journals Adaptive neuro-fuzzy controller of switched reluctance motor

2007 ◽  
Vol 4 (1) ◽  
pp. 23-34 ◽  
Author(s):  
Ahmed Tahour ◽  
Hamza Abid ◽  
Ghani Aissaoui
Keyword(s):  
Fuzzy Inference ◽  
Fuzzy Controller ◽  
Expert Knowledge ◽  
Digital Simulation ◽  
Switched Reluctance Motor ◽  
Motor Speed ◽  
Inference System ◽  
Switched Reluctance ◽  
Neuro Fuzzy ◽  
Reluctance Motor

This paper presents an application of adaptive neuro-fuzzy (ANFIS) control for switched reluctance motor (SRM) speed. The ANFIS has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. An adaptive neuro-fuzzy controller of the motor speed is then designed and simulated. Digital simulation results show that the designed ANFIS speed controller realizes a good dynamic behaviour of the motor, a perfect speed tracking with no overshoot and a good rejection of impact loads disturbance. The results of applying the adaptive neuro-fuzzy controller to a SRM give better performance and high robustness than those obtained by the application of a conventional controller (PI).

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