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  • Open Access


    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


    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 >

  • Open Access


    On an Optimization Method Based on Z-Numbers and the Multi-Objective Evolutionary Algorithm

    Dong Qiu, Rongwen Dong, Shuqiao Chen, Andi Li

    Intelligent Automation & Soft Computing, Vol.24, No.1, pp. 147-150, 2018, DOI:10.1080/10798587.2017.1327153

    Abstract In this paper, we research the optimization problems with multiple Z-number valued objectives. First, we convert Z-numbers to classical fuzzy numbers to simplify the calculation. A new dominance relationship of two fuzzy numbers based on the lower limit of the possibility degree is proposed. Then according to this dominance relationship, we present a multi-objective evolutionary algorithm to solve the optimization problems. Finally, a simple example is used to demonstrate the validity of the suggested algorithm. More >

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