Feature Extraction Algorithm Based on Evolutionary Deep Learning
The contradiction of comprehensive information and dimension curse is the preliminary problem of network situation awareness in the times of big data.Feature extraction is a mainstream method to dimensionality reduction, but performs not well when solving high-dimension and nonlinear data.Deep learning is a multi-layer and nonlinear algorithm which can realize the approximation of complicated function, however, it is sensitive to parameters related to hidden layer.Based on above analysis, a feature extraction algorithm based on evolutionary deep learning was proposed.The algorithm combines evolutionary algorithm(EA) and deep learning, takes advantage of the characteristics of GA and ES,and optimizes the learning structure and relevant parameters.Theoretical analysis and simulation results both prove the effectiveness of this algorithm.
NETL
NSTL
万方数据
维普
