Abstract
In order to solve the problems of poor portability, complex implementation, and low efficiency in the traditional parameter training of the Belief rule-base, an artificial bee colony algorithm combined with Gaussian disturbance optimization was introduced, and a novel Belief rule-base parameter training method was proposed. By the light of the algorithm principle of the artificial bee colony, the honey bee colony search formula and the cross-border processing method were improved, and the Gaussian disturbance was employed to prevent the search from falling into a local optimum. The parameter training was implemented in combination with the constraint conditions of the Belief rule-base. By fitting the multi-peak function and the leakage detection experiment of oil pipelines, the experimental error were compared with the traditional and existing parameter training methods to verify its effectiveness.
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This research was supported by the National Natural Science Foundation of China (Nos. 71501047 and 61773123).
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Su, MN., Fang, ZJ., Ye, SZ., Wu, YJ., Fu, YG. (2018). An Optimized Artificial Bee Colony Based Parameter Training Method for Belief Rule-Base. In: Xu, Z., Gao, X., Miao, Q., Zhang, Y., Bu, J. (eds) Big Data. Big Data 2018. Communications in Computer and Information Science, vol 945. Springer, Singapore. https://doi.org/10.1007/978-981-13-2922-7_5
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DOI: https://doi.org/10.1007/978-981-13-2922-7_5
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