摘要
针对影响管道腐蚀速率预测精度出现的BP算法易陷入局部最小值、收敛速度慢和引起振荡效应等问题,根据改进自适应遗传算法在广泛的空间搜索和向最优解的方向尽快收敛于最优目标的特点,提出了使用改进自适应遗传算法优化BP神经网络,构建了优化的混合算法神经网络模型。实际应用表明,该模型将大大提高网络的学习效率和预测评判的准确率。
The BP shortcomings are apt to fall into some minimum,slowing to converge and causing the effect of shaking, influencing its application to the prediction of the pipeline corrosion rate. According to the characteristic of the improved adaptive GA—EIAGA in extensive space search and converging to the optimum goal as soon as possible in the optimum direction, this paper points out that the improved adaptive GA—IAGA should be used to optimize the BP neural network and structure the optimized mix algorithm neural network model. The application of the optimized model to the pipeline corrosion rate prediction shows that it will prove greatly the learning effectively and the accuracy in prediction and judging.
出处
《油气储运》
CAS
北大核心
2006年第4期34-37,58,共5页
Oil & Gas Storage and Transportation
关键词
管道
BP神经网络
优化
腐蚀速率
预测
应用
pipeline,BP neural network,optimization,corrosion rate,prediction,application
