Notice of RetractionFuzzy Bayesian Networks and its application in pressure equipment´s security alerts

Notice of Retraction

After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.

We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.

The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.

Because attribute variables, namely nodes of Bayesian Networks (BN) may have the characteristics of fuzziness and randomness simultaneously, a Fuzzy Bayesian Network (FBN) algorithm is proposed in this paper. We define fuzzy probability and Conditional Fuzzy Probability Table (CFPT) to express the relationship among variables having mixed uncertainty. We use genetic algorithm to optimize structure learning and parameters learning, feedback to find the optimal network structure according to reasoning error, and fix network parameters at the same time by modifying the parameters of membership function. Finally, we use the FBN algorithm to build the fuzzy Bayesian network and to knowledge reasoning on the data of industrial boilers´ security alerts. Results demonstrate that FBN algorithm applied in mixed uncertainty problems is more effective compared with existing BN algorithms.