Though choosing the experimental results as the learning sample, the performance predictive model of EDM micro-and-small holes is proposed, with the BP algorithm of artificial neural network.
采用人工神经网络的BP算法，以电火花微小孔加工工艺参数正交实验的结果作为神经网络的学习样本，建立电火花微小孔加工多目标工艺参数的预测模型。
According to the unified and standardization criterion, the optimal objectives are graded and combined to form learning sample set for FNN model, and its output is the integrated evaluated value.
采用统一、规范化准则，对各优化目标进行等级划分并进行不同的组合，形成FNN的学习样本集，其输出即决策解的综合性能的评估值。
An improved BP algorithm and a learning sample generation method based on statistics are performed.
提出了一种基于统计的学习样本生成方法，使样本生成问题规范化。
The part of neural network of the intelligent self-tuning PID controller based on BP network learns the learning sample.
由设计出的基于BP神经网络的智能自整定PID控制器的神经网络部分对学习样本进行学习。
The part of neural network of the intelligent self-tuning PID controller based on BP network learns the learning sample.
由设计出的基于BP神经网络的智能自整定PID控制器的神经网络部分对学习样本进行学习。
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