基于深度学习特征迁移的装备体系效能预测

针对武器装备体系效能评估在高维噪声小样本数据条件下准确性不高的问题,提出一种基于堆栈降噪自编码与支持向量回归机的混合模型。利用堆栈自编码神经网络对通用深层特征的自主抽取能力,通过在相似源域大数据上预训练混合模型,获得两任务间的共有特征知识,借助对该知识的迁移,在目标域微调该混合模型,从而提升支持向量回归机在小样本噪声数据上的学习预测精度。在一定作战想定背景下,结合武器装备体系仿真试验数据,对该混合模型进行验证。实验结果表明,与传统支持向量回归机等模型相比,所提模型能够更准确地评估装备效能。

Effectiveness prediction of weapon equipment system of systems based on deep learning feature transfer#br#

In order to improve the prediction accuracy of effectiveness of weapon equipment system-of-systems which trains on high dimensional and noisy small samples, a hybrid model based on stacked denoising auto encoder (SDA) and support vector regression (SVR) is proposed. By taking the advantage of SDA, the method extracts common features autonomously on related but different domain data. This hybrid model is pre trained by using a large number of source domain data. Then the hybrid model is transferred as prior knowledge on target domain. By transferring these prior knowledge, the hybrid model is fine tuned on high dimensional and noisy small target domain data, making up for the defects of traditional SVR. In a certain battle scenario, the model with the simulation data produced by simulation testbed is validated. Experimental results demonstrate the effectiveness of the proposed model.