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Research on the ideas and models for discriminating the order parameters of complex self-organizing system: Taking the industrial convergence system of EMI and PS as example

Liang-Qun Qi, Yuan-Yuan Cai, Cheng-Dong Wang

Complex self-organizing systems are widely exists in the social and economic fields, and most of them have the typical characteristics of complex system. The order parameter plays a very important role in the evolution process of complex self-organizing system. Therefore, the discrimination of the order parameters becomes a critical issue for revealing the development laws of the complex self-organizing system. This paper designs the ideas and models for discriminating the order parameters of complex self-organizing system based on cluster analysis and rough set theory, and carries out an empirical study for proving the correctness and effectiveness of them. The cluster analysis is used for determining the “state” of both complex self-organizing system and its macro control parameters, and building up the knowledge set which is composed of corresponding rules between their “states”. And the rough set theory is used for reducing the macro control parameters of complex self-organizing system based on the knowledge set. This study shows that, the ideas for discriminating the order parameters based on the “state” of both complex self-organizing system and its macro control parameters is feasible, and the models for discriminating the order parameters of complex self-organizing system on the basis of clustering analysis and rough sets theory is particularly effective.