Open AccessDesign and Application of Model Based on Bayesian Network
Author(s) -
Ying Shi
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2074/1/012037
Subject(s) - computer science , crossover , bayesian network , inference , fitness function , artificial intelligence , machine learning , coding (social sciences) , genetic algorithm , bayesian inference , bayesian probability , mathematics , statistics
At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.