油气管道腐蚀失效检测具有随机性、复杂性、多因素性和非线性等特点,利用精确的数学模型描述有一定的难度。本文提出了一种基于混合改进粒子群算法的模糊神经网络的管道腐蚀动态检测方法。优化粒子群算法的收敛性,加快寻找最优解的速度,将该算法用于模糊神经网络模型构建中,建立了基于模糊神经网络的管道腐蚀动态检测模型。通过利用实际的管道腐蚀检测数据进行诊断应用,取得了较好的检测效果,验证了该模型及算法的可行性和有效性。%The failure detection of the oil and gas pipeline corrosion involve many characteristics, such as randomness, complex, multiple factors, and non-linear so on, it is difficult to describe by using the precise mathematical model. This paper proposes a failure detection method on pipeline corrosion, which is the Fuzzy neural network, based on Hybrid the improved particle swarm algorithm. It optimizes the convergence of Particle swarm algorithm and accelerates the speed of finding the optimal solution, which is used in the building of the Fuzzy neural network, and in this way, it sets up a pipeline corrosion failure detection model. It obtains a good recognition effect, by using the actual pipeline corrosion failure detection data to diagnostic applications, and the result verify the feasibility and validity of the model and algorithm.
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