Membrane structure inhibitors of decision evolution sets
Hu Yuwen1，2，3*，Xu Jiucheng1，2，Zhang Qianqian1，2
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About authors:
1.College of Computer and Information Engineering，Henan Normal University，Xinxiang，453007，China；
2．Engineering Technology Research Center for Computing Intelligence & Data Mining of Henan Province，Xinxiang，453007，China；3.Library，Henan Normal University，Xinxiang，453007，China
The decision evolution sets is a theory and a method used for solving the evolvement of the decision rules in time series. The decision evolution sets shows the evolvement model of the decision rules in time series，but it is difficult to draw the evolution track of the decision rules under standard definitions. The membrane structure of the decision evolution sets is a theory and a method，which concretes the evolution track based on the decision evolution sets. It uses membrane structure theory to rebuild the decision evolution sets in a new way，and concrets the hidden attribute，missing attribute，evolution angle，deviation angle and predicted angle which are under the decision evolution sets. However，under the standard membrane structure of the decision evolution sets，at the process of concreting the evolvement track of the decision rules，the predicted membrane and the real membrane only show the relevance of deviation angle，which produces a problem that the reason of how the predicted membrane transformed into the real membrane had been hidden. To solve the problem that how the predicted membrane transformed into the real membrane，this paper brings in the concept of inhibitors to rebuild the standard membrane structure of the decision evolution sets. This paper also shows the properties of the membrane structure of the decision evolution sets which has inhibitors，and further explains and concretes the process of the transformation between the predicted membrane and the real membrane.
Hu Yuwen1，2，3*，Xu Jiucheng1，2，Zhang Qianqian1，2.
Membrane structure inhibitors of decision evolution sets[J]. Journal of Nanjing University(Natural Sciences), 2018, 54(4): 810
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