Hopping sequence prediction system based on graphical model

Abstract

The invention discloses a hopping sequence prediction system based on a graphical model. The system comprises a preprocessing module, a prediction module and a feedback adjusting module, wherein the preprocessing module is used for removing noise and bandwidth from an intercepted original hopping sequence; the prediction module is connected with the preprocessing module and used for reconstructing a phase space and constructing a prediction model; and the feedback adjusting module is connected with the preprocessing module and the prediction model and used for precision detection, feedback and model adjustment. According to the system, embedded dimension m and time delay phi are solved by adopting a Cao method and an autocorrelation method, and then the phase space is reconstructed; and the Markov boundary of query nodes is learnt on the basis of an improved Microsoft malware protection center (MMPC) algorithm, and then the prediction model is constructed. The embedded dimension m and the time delay phi are two key parameters for reconstructing the phase space, and the parameters acquired by using the autocorrelation method and the Cao method are stable and reliable; and a Bayesian network model is simplified through the Markov boundary, so that the prediction efficiency is high.

Description

technical field [0001] The invention belongs to the technical field of frequency hopping sequence prediction, in particular to a graph model-based frequency hopping sequence prediction system. Background technique [0002] The existing chaotic time series prediction method is to embed the original chaotic time series into the m-dimensional phase space. Construct a phase point in the m-dimensional phase space, because the selection of the time delay τ can ensure that these time delay points are correlated, so the Bayesian network can be used to describe this correlation; [0003] The reconstructed phase space is expressed as an m×n-dimensional matrix, and there is a dependency correlation between each row vector. The existing technical solution: [0004] (1) Take each row vector as a variable of the Bayesian network, use the K2 algorithm to learn the Bayesian network structure, and then construct a directed acyclic graph containing m nodes. The directed acyclic graph takes e...

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Problems solved by technology

[0014] The purpose of the embodiment of the present invention is to provide a frequency hopping sequence prediction system based on a graphical model, which aims to solve the problem of evaluating the time delay τ and embedded w

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