Network cognition self-adaptive algorithm based on Bayesian network

Abstract

The invention provides a network cognition self-adaptive algorithm based on a Bayesian network. The network cognition self-adaptive algorithm comprises steps as follows: generating initial problem solution groups randomly from all feasible solutions according to uniform distribution; when a self-adaptive algorithm end condition is not satisfied, selecting solution groups with superior adaptive values from the current solution groups according to a selection strategy, constructing the Bayesian network on the basis of the current superior solution groups, and constructing a Bayesian network probability model; when self-adaptive reasoning is not ended, continuously using a current Bayesian network model for reasoning to obtain superior solutions; when self-adaptive reasoning is ended, replacing certain individuals in the current solution groups with newly generated candidate solutions. The self-adaptive strategy is introduced in the algorithm, so that the Bayesian optimization algorithm can perform reasoning by fully using the established Bayesian network probability model and can explore certain unknown areas, the good experiment effect can be realized without a large quantity of credible instance data, and the effectiveness and the reliability of the algorithm are improved.

Description

technical field [0001] The invention relates to a reasoning algorithm, in particular to a network cognitive adaptive algorithm based on a Bayesian network. Background technique [0002] The traditional network is a platform for information transmission, reception, and sharing, through which users can link various rich information together to realize information sharing between different regions. The cognitive network is a cutting-edge communication technology newly proposed in recent years. It comprehensively uses technologies such as perception, learning, and reconfiguration, so it has important application value. Cognitive network is a network with a cognitive process. By perceiving the current network environment, after its own understanding and learning, it adjusts its internal configuration to adapt to changes in the external network based on the understanding and learned knowledge. That is, the cognitive network can continuously learn and accumulate relevant knowledge...

AI Technical Summary

Problems solved by technology

[0011] 1. Most of these studies are aimed at a certain local and specific control method, and it is difficult to upgrade it to a method and mechanism with global significance;

[0012] 2. The existing research results lack the overall assessment of the network situation, and the lack of understanding of the cognitive ability, knowledge level and other personality characteristics of the network level (learners);

[0013] 3. It cannot fully meet the different needs of users, and cannot provide learners with personalized reconfiguration data support and guidance

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