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《Proceedings of the 4th International Conference on Quality and Reliability》2005年
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ROUGH SETS APPROACHES FOR EQUIPMENT SERVING STRATEGIES BASED ON DISCRETE MARKOV CONTROL AND DECISION PROCESS

HONGSHENG SU  
【摘要】:正 Based on markov control and decision process with discrete and countable states and the relations between probability and rough sets, this paper establishes discrete markov control and decision theory on rough sets model for a stochastic and controlled decision system. Namely, system state of arbitrary given time is stochastic, rule sets of homogenous state may be diverse in different time, but state numbers and rule sets are limited, utility function of each rule is finite, the aim is to seek the best decision rule sets to drop risk loss to the minimum degree provided that initial states distributions are given, therefore, which is a half-markov random decision process. Conventional means applies probabilistic reasoning for evaluating risk loss to constitute action rule sets, which is very complex and low efficient. Based on points, the article establishes risk evaluation methods on rough sets model, i.e. supposing that utility/risk function of each rule is given, the aim is to select the best action rule for the equivalent class[x] with the lowest risk loss by bayes estimation, where the equivalent class[x] represents object of universe with probability space and expresses any random state. For any given time segment if initial state distributions of system are given, the best decision rule sets in diverse time can be acquired. The study indicates whose rules acquisition is relatively easy and reasoning process is simple. Being used in maintenance strategies for equipments, the results show the method is effective and available, is an intelligent and scientific maintenance strategy.

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