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1.The method of B P algorithm was combined with the simulating annealing algorithm,which makes the selection of networks structure and the determination of momentum parameter α and learning rate η more reasonable. The learning efficiency and the calculating precision was improved. The result proved availble for finding up an optimum heat treatment regime and for component part design for high strength high fracture toughness steel.
用人工神经网络方法对高合金高强高韧钢的性能优化问题进行了研究.并结合模拟退火算法对B?P算法进行了改进,使得网络结构的选取、动量项系数α及学习率η的确定更加合理,提高了网络的学习效率,改善了预测精度.研究结果可用于高强高韧钢的成分设计及热处理工艺的优化.收藏指正
2.Experimental results show that the phase fraction error and counting error arose fromthe dynamic change is a function of the change rate of each phase medium andmedium absorb coefficient. When the speed flow and the flow regime change in thepipeline, it is difficult to eliminate the influence on the dynamic error by decreasingdetecting time. The key is that the reasonable measuring time should be determinedaccording to the flow regimes.
实验表明,由动态变化引起的相含率误差与计数误差是流体各相介质的变化率和介质吸收系数的函数,在流速较快并且流型变化的输油管道内,通过减少测量时间难以消除动态误差的影响,关键应结合流型变化正确地选择测量时间;收藏指正
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