关键词: 神经网络, 态势评估, 深度自动编码器, 深度自编码网络, 标签

Abstract:

Aiming at the defect of the dependence of BP neural network on label data, a situation assessment method based on deep automatic coding network is proposed. The model uses depth auto-encoder as the basic unit to construct depth auto-coding network, and trains depth auto-coding network with expert experience and hierarchical evaluation method. The network is pre-trained by unsupervised layer-by-layer algorithm using unlabeled data to determine the range space of parameters and weights of each layer of the network. Based on this, the network is fine-tuned by using labeled samples with supervised algorithm so that the parameters and weights of each layer are optimized. As a result a model with the ability to accurately evaluate the input situation data is formed. Compared with BP neural network, the deep auto-coding network model is less affected by labels, which significantly reduces the dependence on expert experience, and has a higher overall evaluation accuracy.

Key words: neural network, situation prediction, deep automatic encoder, deep auto-encoders network, tag