Research on the effectiveness evaluation of big data in combat simulation

With the development of complex system simulation techniques, computational capabilities, and data management capabilities, the simulation results tend to be big data. There are also high-dimension, high-redundancy, and high-correlation issues among indexes. Based on the above background, a two-layer Autoencoder neural network is used for feature extraction and dimensionality reduction. Then, 10 deep neural network models are established for index learning. The experimental results show that the 32-layer Resent network works best for low-dimensional data effectiveness evaluation.