The neural networks structure design, learning samples and training algorithms are expounded.
阐明了神经网络状态选择器的结构设计、样本选取及训练方法。
Secondly, to extract learning samples from the MADM problem, an approach to estimate the utility functions for attributes is presented.
其次,提出了基于属性效用函数估计的学习样本构造方法,从决策问题本身抽取学习样本。
The affection of learning samples and network parameters on prediction accuracy was discussed, the best network parameters were selected.
讨论了模型的学习样本、网络参数对预测精度的影响,选出最佳网络参数配置。
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