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  • Open Access

    ARTICLE

    New Multi-layer Method for Z-number Ranking Using Hyperbolic Tangent Function and Convex Combination

    Somayeh Ezadia, Tofigh Allahviranloob

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 217-221, 2018, DOI:10.1080/10798587.2017.1367146

    Abstract Many practical applications, under the definitive evolutionary state of the nature, the consequences of the decisions, mental states of a decision maker are required. Thus, the need is for a new concept in the analysis of decision-making. Zadeh has introduced this concept as the Z-number. Because the concept is relatively new, Z-number in fuzzy sets, hence, its basic theoretical aspects are yet undetermined. This paper presents a method for ranking Z-numbers. Hence, we propose a new method for ranking fuzzy numbers based on that of hyperbolic tangent function and convex combination. Then, using the same technique we propose a method… More >

  • Open Access

    ARTICLE

    Introduction to U-Number Calculus

    R. A. Alieva,b

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 211-216, 2018, DOI:10.1080/10798587.2017.1330311

    Abstract Commonsense reasoning plays a pivotal role in the development of intelligent systems for decisionmaking, system analysis, control and other applications. As Prof. L. Zadeh mentions a kernel of the theory of commonsense is the concept of usuality. Zadeh suggested main principles of the theory of usuality, unfortunately up to present day; a fundamental and systemic approach to reasoning with usual knowledge is not developed.
    In this study, we develop a new approach to calculus of usual numbers (U-numbers). We consider a U-number as a Z-number, where the second component is “usually”. Validity of the suggested approach is verified by examples. More >

  • Open Access

    ARTICLE

    Z-Numbers and Type-2 Fuzzy Sets: A Representation Result

    R. A. Alieva,b, Vladik Kreinovichc

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 205-210, 2018, DOI:10.1080/10798587.2017.1330310

    Abstract Traditional [0; 1] based fuzzy sets were originally invented to describe expert knowledge expressed in terms of imprecise “fuzzy” words from the natural language. To make this description more adequate, several generalizations of the traditional [0; 1] based fuzzy sets have been proposed, among them type- 2 fuzzy sets and Z-numbers. The main objective of this paper is to study the relation between these two generalizations. As a result of this study, we show that if we apply data processing to Z-numbers, then we get type-2 sets of special type —that we call monotonic. We also prove that every monotonic… More >

  • Open Access

    ARTICLE

    Numerical Solution of Linear Regression Based on Z-Numbers by Improved Neural Network

    Somayeh Ezadia, Tofigh Allahviranloob

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 193-204, 2018, DOI:10.1080/10798587.2017.1328812

    Abstract In this article, the researcher at first focuses on introducing a linear regression based on the Z-number. In this regression, observations are real, but the coefficients and results of observations are unknown and in the form of Z-rating. Therefore, to estimate this type of regression, we have three distinct ways depending on different conditions dominating the problem. The three methods are a combination of artificial neural networks and fuzzy generalized improvements of the technique. Moreover the method of calculating the weights of the Z-number neural network has been mentioned and the stability of neural network weights is considered. In some… More >

  • Open Access

    ARTICLE

    A Z-Number Valued Regression Model and Its Application

    Lala M. Zeinalovaa, O. H. Huseynovb, P. Sharghic

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 187-192, 2018, DOI:10.1080/10798587.2017.1327551

    Abstract Regression analysis is widely used for modeling of real-world processes in various fields. It should be noted that information relevant to real-world processes is characterized by imprecision and partial reliability. This involves combination of fuzzy and probabilistic uncertainties. Prof.. L. Zadeh introduced the concept of a Z-number as a formal construct for dealing with such information. The present stateof-the-art of regression analysis under Z-number valued information is very scarce. In this paper we consider a Z-number valued multiple regression analysis and its application to a real-world decisionmaking problem. The obtained results show applicability of the proposed approach. More >

  • Open Access

    THEORY

    Zet Theory

    Mark J. Wierman

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 179-186, 2018, DOI:10.1080/10798587.2017.1327160

    Abstract The theory of Zets is presented and the standard techniques of set theory allows for the development of a rich algebra of Zets. It shows that Zets and fuzzy sets are essentially interchangeable. However, the fundamental manipulations, techniques, and definitions of Zets are simple and more amenable to analyze. For example, the extension principle is easy to define. More >

  • Open Access

    ARTICLE

    Modeling of Consumer Buying Behaviour Using Z-Number Concept

    Gunay Sadikoglu

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 173-178, 2018, DOI:10.1080/10798587.2017.1327159

    Abstract Consumer behaviour has always been of a great interest in marketing research. The consumer buying behaviour has become an integral part of strategic market planning and includes mental, emotional and physical activities. The consumer behaviour and decision-making process are usually subject to uncertainties related to influences of socio-cultural, psychological and personal factors. In this paper, the Z-number concept is applied for handling uncertainties in analysing the consumer buying behaviour. More >

  • Open Access

    ARTICLE

    Failure Mode and Effects Analysis Based on Z-numbers

    Wen Jiang, Chunhe Xie, Boya Wei, Yongchuan Tang

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 165-172, 2018, DOI:10.1080/10798587.2017.1327158

    Abstract The main objective of this paper is to propose a new method for failure mode and effects analysis (FMEA) based on Z-numbers. In the proposed method, firstly, Z-numbers are used to perform the valuations (Z-valuation) of the risk factors like occurrence (O), severity (S) and detection (D). Secondly, the Z-valuations of the risk factors are integrated by fuzzy weighted mean method. A new risk priority number named as ZRPN is calculated to prioritize failure modes based on a modified method of ranking fuzzy numbers. Finally, a case study for the rotor blades of an aircraft turbine is performed to demonstrate… More >

  • Open Access

    ARTICLE

    The Identification of Job Satisfaction under Z-Information

    S. Z. Eyupoglua, K. I. Jabbarovab, K. R. Aliyevac

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 159-164, 2018, DOI:10.1080/10798587.2017.1327156

    Abstract Complexities in organizational and economical environments have lead psychologists, management scholars, and economists to investigate the multi-dimensional essence of job satisfaction. Unfortunately, existing studies are based on exact data, whereas relevant information is imperfect. To deal with imprecise and partially reliable information, Zadeh proposed the concept of a Z-number. In this paper we consider the Z-number valued rule based model to represent the relationship between job satisfaction and the facets/factors influencing job satisfaction. A real-world job satisfaction index evaluation problem is used to illustrate the suggested approach More >

  • Open Access

    ARTICLE

    Numerical Solution of Fuzzy Equations with Z-numbers Using Neural Networks

    Raheleh Jafaria, Wen Yua, Xiaoou Lib

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 151-158, 2018, DOI:10.1080/10798587.2017.1327154

    Abstract In this paper, the uncertainty property is represented by the Z-number as the coefficients of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. We also extend the fuzzy equation into dual type, which is natural for linearin-parameter nonlinear systems. The solutions of these fuzzy equations are the controllers when the desired references are regarded as the outputs. The existence conditions of the solutions (controllability) are proposed. Two types of neural networks are implemented to approximate solutions of the fuzzy equations with Z-number coefficients. More >

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