Safety monitoring data classification method based on wireless rough network of neighborhood rough sets

The problem of accurate classification of wireless sensor network data is studied. The data attributes of wireless sensor network are highly redundant. The traditional BP neural network is easy to fall into local optimal solution, poor generalization ability and slow convergence. With the low precision and other problems, it is difficult to accurately classify the data, and a classification algorithm using neighborhood rough sets is proposed. The neighborhood rough set model applies the rough set theory to the neighborhood system. Based on the sample points and their neighborhood radius, the distribution of the entire unbalanced data set in the feature space can be easily obtained. The simulation results show that the algorithm accelerates the convergence speed of the network, and the accuracy of sensor network data classification and recognition has also been greatly improved.