A Two-layer Bayesian Network Inference Algorithm Based on Reinforcement Learning Algorithm

Abstract

The present invention provides a double-layer Bayesian network inference algorithm based on an enhanced learning algorithm, comprising the following steps: step S1, initializing the enhanced learning probability table of nodes; step S2, updating the condition selection in the direction of the horizontal axis and the direction of the vertical axis respectively Probabilistic; step S3 , judging the value combinations on the horizontal axis and the nodes on the vertical axis, and deleting redundant value combinations and nodes. The double-layer Bayesian network inference algorithm based on the reinforcement learning algorithm of the present invention models the probability dependence relationship between the two-layer network parameters, analyzes the subsequent network state according to the known network state reasoning, and adopts the reinforcement learning algorithm to infer the reasoning The uncertain information of the network nodes obtained in the process is learned and judged, and then classified to obtain its probability reliability value, and the obtained double-layer Bayesian network model is further simplified, and only the most useful for reasoning are retained. information, making it more conducive to implementation and accurate reasoning.

Description

technical field [0001] The invention relates to a reasoning algorithm, in particular to a double-layer Bayesian network reasoning algorithm based on a reinforcement learning algorithm. Background technique [0002] Cognition of the network is to adjust the corresponding configuration inside the network to adapt to changes in the external environment by perceiving the external environment and through self-understanding and learning. The cognitive process is a process of continuously learning and accumulating relevant experience in the process of dynamic self-adaptation, and using this as a basis to make relevant adjustments, judgments, and reconfigurations to the network. The adaptive dynamic adjustment process occurs before the problem occurs, not after, so the performance improvement of the network focuses on the end-to-end Quality of Service (QoS) performance of the entire network. Due to the above characteristics, traditional network cognition can provide users with bett...

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 to a method and mechanism with global significance;

[0012] (2) The existing research results lack a global assessment of the network situation, and 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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