因此，否定效应是支持记忆的层次网络模型的有力证据。
Therefore, the negative effects are the powerful supportive verification for hierarchical network models of memory.
文中用作者提出的通用前馈网络和排序学习算法，提出了一种设计具有期望容错域的前向掩蔽联想记忆模型的方法。
A design method of ahead masking associative memory model with expecting fault-tolerant field is proposed by use of the general feed-forward network and sequential learning algorithm given by authors.
神经网络具有良好的记忆、归纳和学习能力，对难以用数学方法建立精确模型的信息、工艺等能够进行有效地预测建模。
The neural network has the abilities of memory, induction & study , it can effectively build forecasting model for information & technics which are difficult to be built into an exact math model.
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