BP neural networks with pattern extended input are used to estimate control parameters, and the learning speed is increased.
并采用具有模式增强输入的BP网络进行决策参数估计，加快学习的收敛。
The simulation results show that the learning and generalization capability of the new models are much better than the conventional BP networks.
几个典型实验的结果表明，与传统BP网络模型相比，新网络模型在学习能力和泛化推广能力方面都有明显提高。
The BP neural network combines with the SOM neural networks to form a intrusion detection learning model with relatively complete function.
将BP神经网络和SOM神经网络结合起来，组建功能相对完善的入侵检测学习模型。
Learning algorithm is the core of the subject of studying BP feedforward neural networks.
学习算法是BP前馈神经网络研究中的核心问题。
Since we value the learning effect of neural networks by cumulative error, the paper pay direct attention to it to study the BP algorithm.
由于评价人工神经网络最终学习效果是通过累积误差来进行的，从而我们直接瞄准累积误差来研究多层人工神经网络快速学习的算法。
The PID controller based on BP neural networks is designed to realize control parameter self-learning and self-adjusting.
设计了基于BP神经网络的PID控制器，实现PID控制器参数自学习、自整定。
The PID controller based on BP neural networks is designed to realize control parameter self-learning and self-adjusting.
设计了基于BP神经网络的PID控制器，实现PID控制器参数自学习、自整定。
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