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训练次数的英文

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"训练次数"怎么读用"训练次数"造句

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  • frequency of training

例句与用法

  • The result of emulational experiment shows that the algorithms are effective . the algorithm solves the problems of too much training and the low precision of bp network applied in solder joints inspection
    仿真实验验证了改进后的算法是有效的,并解决了将bp网络应用于基于结构式光源的锡量类型焊点质量检测中时训练次数较多和网络精度不高的问题。
  • Simulation results indicate that the method is very effective to robotic system with unknown external disturbances , and it can also acquire satisfying tracking performance by fewer numbers of network training an d iterative learning processes
    仿真结果表明,该方法对有未知外部干扰的机器人系统是十分有效的,且能以极少的网络训练次数和迭代学习次数达到满意的跟踪性能。
  • We deduce frondose algorithm of three layers bp neural networks which is used in common , and discuss several important issues in designing neural networks which is used to forecast , for example , number of hidden layer , nerve cell number of hidden layer , epoch of learning , embryonic power value , decision of node number about input and outputo at the same time , this dissertation sums up things that conventional bp algorithm is improved on considering disadvantages of it
    3推导了常用的三层bp神经网络具体算法,讨论了实际预测应用中神经网络设计方面的几个重要问题,如隐层数、隐层神经元数、训练次数、初始权值、输入节点数以及输出节点数的确定。同时,针对传统bp算法存在的各种各样的缺点,文中综述了对其改进的情况。
  • The purpose of this thesis is to use artificial neural networks ( ann ) to identify the aerodynamic parameters . first introduced in this paper is the basic theory of ann and wind engineering in bridge , then ann is presented to identify the aerodynamic derivatives of ideal thin plate . a back - propagation ( bp ) ann is established and trained for many times to contrast the influences of some factors on the prediction results
    首先对人工神经网络和桥梁风工程的基本理论作了简单的介绍,接着采用人工神经网络识别了理想薄平板的气动导数,并分析了数据处理方式、隐层单元数、训练次数、随机赋值次数、样本数量对训练结果的影响,讨论了训练的稳定性。
  • The principle of the evaluation system is described in details , included : defining the layer numbers of hidden neurons , defining the processing unit numbers of hidden neurons , defining the train numbers and selecting original value ; input and output parameters determination etc . the bp net can study and story a lot of mapping relationship of input - output model , and need not mathematics describing of mapping relationship in advance
    论文详细论述了评估系统的设计过程:隐层数的确定、隐层单元数的确定、训练次数的确定、初始值的确定和输入输出参数的确定等。 bp网能学习和存贮大量的输入-输出模式映射关系,而无需事前揭示描述这种映射关系的数学方程,只需用足够多的模式对对网络进行训练即可。
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