Research Area: Metaheuristic Computing
In order to improve integrity management evaluation level of pipeline, the fuzzy surfacelet neural network optimized by improved dragonfly algorithm. Firstly, the domestic and foreign related research progresses of pipeline integrity management are summarized. Secondly, the pipeline integrity management evaluation index system is constructed according to management and technology characteristics of pipeline integrity management. Thirdly, the fuzzy surfacelet neural network with five layers is established by combining Surfacelet transfer, wavelet neural network and fuzzy theory. The improved dragonfly algorithm is established by improved population initialization strategy and inertia weight updating strategy. Finally, simulation analysis of pipeline integrity management is carried out based on fuzzy B-spline wavelet neural network optimized by improved particle swarm algorithm (BWNN-IPSA), fuzzy surfacelet neural network optimized by traditional dragonfly algorithm (FSNN-TDA) and fuzzy surface neural network optimized by improved dragonfly algorithm (FSNN-IDA), simulation results show that the fuzzy Surfacelet neural network optimized by improved dragonfly algorithm can achieve convergence after 500 times, it has less convergence times than other evaluation models. The mean square error of the proposed evaluation model ranges from 0.79 to 1.02%, it has less error than other evaluation models. Therefore the proposed fuzzy surface neural network optimized by improved dragonfly algorithm has higher computing precision and efficiency. The proposed evaluation model of pipeline integrity management can improve intelligent level of pipeline management, and ensure the safety and reliability of pipeline system.
Keywords:
integrity management
evaluation level
pipeline
fuzzy surfacelet
neural network
Author(s) Name: Jiaming Sun, Bin Zhao, Diankui Gao, Lizhi Xu
Journal name: Applied Soft Computing
Conferrence name:
Publisher name: Elsevier
DOI: 10.1016/j.asoc.2021.107862
Volume Information: Volume 113
