Network abnormal flow prediction method based on improved radial basis function neural network algorithm

Abstract

The invention provides a network abnormal flow prediction method based on an improved radial basis function neural network algorithm. The method comprises the steps of initializing an RBF neural network, optimizing RBF neural network parameters by adopting a QAPSO algorithm, reconstructing an optimized RBF neural network prediction model according to the optimal parameters of the RBF neural network, and performing flow prediction by using the optimized RBF neural network prediction model. The invention aims to solve the problems of premature convergence, easiness in falling into a local optimal solution, insufficient search precision and the like of a PSO algorithm. On the basis of an APSO algorithm based on a self-adaptive thought and a QPSO algorithm based on a quantum theory, an APSO algorithm and a QPSO algorithm based on a quantum theory are combined. According to the method, a QAPSO algorithm with higher global convergence capability and global search precision is designed by combining the two algorithms, and RBF algorithm parameters are optimized by using the QAPSO algorithm, so that the prediction precision of the RBF neural network is improved.

Description

technical field [0001] The invention relates to the field of network traffic early warning, in particular to a network abnormal traffic prediction method based on an improved radial basis neural network algorithm. Background technique [0002] At present, the global power field has entered the era of intelligence, and ensuring the security of the smart substation communication system based on IEC61850 plays an increasingly important role in the safe operation of the smart grid. [0003] Network traffic early warning is an important part of power security protection, and a stable and high-precision abnormal traffic early warning system based on this will affect the safe operation and production of smart substations. Network traffic forecasting is the premise of abnormal network traffic early warning. It mainly includes two parts: extracting traffic characteristics and constructing a suitable forecasting model. Researching network traffic forecasting model is of great signific...

AI Technical Summary

Problems solved by technology

Smart substations often have large fluctuations in communication traffic due to emergencies, which have strong randomness

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