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Network security situation analysis method based on iteratively optimized rbf neural network

A neural network and network security technology, applied in the field of network security, can solve problems such as the inability to achieve the correct direction of convergence, the inability to guarantee the population convergence direction and convergence speed, and the slow convergence speed. The effect of precision

Active Publication Date: 2022-03-15
ZHEJIANG UNIV +1
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Problems solved by technology

[0002] The crossover operation of the traditional genetic algorithm comes from individuals of different categories, and does not consider that only the operation between different individuals cannot guarantee the convergence direction and convergence speed of the entire population, so in many cases the two parameters of the RBF neural network cannot reach the convergence In the right direction, and the convergence speed is slow

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  • Network security situation analysis method based on iteratively optimized rbf neural network
  • Network security situation analysis method based on iteratively optimized rbf neural network
  • Network security situation analysis method based on iteratively optimized rbf neural network

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Embodiment Construction

[0049] In response to the analysis and prediction of the RBF neural network in the network security situation, there is insufficient pre-accuracy and robustness, and the invention is based on the genetic algorithm based on the cross-model, and the algorithm simultaneously considers the genetic operations between the same types, and produces two operations. Individuals join the competition mechanism, performing a survival of the generated individual individuals, and selecting the individuals with strong adaptivity into the next generation, through the continuous iteration of two parameters of the RBF neural network. In fact, due to the neopes reproduction between the similar individuals in the sub-population can protect excellent genetic mode to some extent, maintain individual excellent traits, speed convergence, but it will cause diversity if excessively protecting excellent individuals Sexual deletion, thereby causing convergence to local minimal values. Therefore, this article ...

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Abstract

The invention discloses a network security situation analysis method based on an iteratively optimized RBF neural network. The present invention iteratively optimizes the width and link weight of the RBF network through the genetic algorithm for the first time, maintains a lower absolute error on the whole, and makes self-adaptive adjustments based on the crossover model and the probability of gene mutation, so that the population iterates in a favorable direction and speeds up The convergence speed of the algorithm and the chaotic search strategy also prevent the algorithm from falling into a local minimum during the iterative process.

Description

Technical field [0001] The invention belongs to the field of network security technology, and more particularly to a network security situation analysis method based on iterative optimization RBF neural network. Background technique [0002] The crossover operation of the traditional genetic algorithm is derived from individuals of different categories, and there is no consideration of only the operation between the different individuals cannot guarantee the convergence direction and convergence speed of the entire population, so many of the two parameters of the RBF neural network cannot achieve convergence. The correct direction and the convergence speed is slow. Inventive content [0003] It is an object of the present invention to provide a network security trend analysis method based on iterative optimization RBF neural network for the shortcomings of prior art. [0004] It is an object of the present invention to be implemented by an iterative optimized RBF neural network,...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04L41/142H04L41/14H04L9/40G06N3/04G06N3/12
CPCH04L41/142H04L41/145H04L63/20G06N3/126G06N3/047
Inventor 吴春明吴玉芹
Owner ZHEJIANG UNIV