In this paper we investigate the dynamic multi-attribute group decision making problems, in which all the attribute values are provided by multiple decision makers at different periods. In order to increase the level of overall satisfaction for the final decision and deal with uncertainty, the attribute values are enhanced with generalized interval-valued trapezoidal fuzzy numbers to cope with the vagueness and indeterminacy. We first define the Dynamic Generalized Interval-valued Trapezoidal Fuzzy Numbers Weighted Geometric Aggregation (DGITFNWGA) operator and give an approach to determine the weights of periods, using the probability density function of Gamma distribution, and then a dynamic multi-attribute group decision making method is developed. The method proposed employs the Generalized Interval-valued Trapezoidal Fuzzy Numbers Hybrid Geometric Aggregation (GITFNHGA) operator to aggregate all individual decision information into the collective attribute values corresponding to each alternative at the same time period, and then utilizes the DGITFNWGA operator to aggregate the collective attribute values at different periods into the overall attribute values corresponding to each alternative and obtains the alternatives ranking, by which the optimal alternative can be determined. Finally, an illustrative example is given to verify the approach developed.
In order to improve diagnosis precision and decreasing misinformation diagnosis, according to the intelligence complementary strategy, a new complex intelligent fault diagnosis method based on rough sets theory and RBF neural network is presented. Firstly, basis on data pretreatment, the fault diagnosis decision table is formed, and continuous datum are discretized by using hybrid clustering method. Rough sets theory as a new mat hematical tool is used to deal with inexact and uncertain knowledge for pattern recognition. The target is mainly to remove redundant information and seek for reduced decision tables which to obtain he minimum fault feature subset. The neural networks adopted were of the feed-forward variety with one hidden layer. They were trained using back-propagation. The method can reduce the false alarm rate and missing alarm rate of the fault diagnosis system effectively, and can detect the composed faults while keep good robustness.
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