SOM Space-Based Resource State Detection Algorithm Based on Feature Subspace
In the space-based information network,the resource monitoring system captures a large amount of data and redundant features of performance indicators,which leads to low accuracy of state detection and long detection time.To address this problem,a SOM state detection algorithm based on feature subspace weighting is proposed.The feature extraction algorithm was embedded into the SOM neural network model,so that the network can extract the at-tribute features corresponding to each category while training,and form the feature subspace corresponding to the state.The contribution of the feature to the category was calculated by using the feature subspace of the state,and the objective function of the SOM neural network was optimized,so as to improve the speed and accuracy of the model for satellite state detection.The experiment simulated the detection accuracy,detection sensitivity and detection time un-der different state detection models.The results show that the state detection model proposed in this paper has good performance in terms of detection accuracy,detection sensitivity and detection time.
Space-based information networkFeature subspaceSOMStatedetection
NETL
NSTL
万方数据
