提出了一种复合神经网络的算法模型，用该模型训练全加器(FA)获得了高精度分类结果。
A model of algorithm has been proposed for composite neural network and the classification results of high precision are obtained through a full adder (FA) fostered by the model.
考虑工业系统故障预知的滞后，软件设计中采用了特殊的复合神经网络结构以便于维护和拓展。
Considered the lag of the fault detection, the design introduces a specific compound network architecture that makes the software convenient for maintainability and extension.
提出了一种用于刀具状态监测的复合神经网络模型，模型由多个神经网络组成，神经网络的数目等于要监测的刀具故障数目。
This paper proposes a composite neural model for tool condition monitoring. It is composed with several neural networks and the number of neural networks is equal to the number of tool faults.
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