Abstract
AbstractConsidering the application background of hydro-electrical simulation, a new algorithm for learning Bayesian network structure is proposed according to the rule base provided by many experts. This algorithm adopts statistical strategy during extracting valid rules from rule base, discards the rules with weak causality, and only retains those with strong causality. Bayesian network topology model is formed by structure learning with these valid rules. One loop-breaking technique is proposed for the causality loop existed in topological structure. And this model is used in the hydro-electrical simulation system of Fengman Hydraulic Power Plant, which exerts the advantage of Bayesian network in solving the uncertainty problem. This model has been proved effective during the actual operation.KeywordsParticle Swarm OptimizationBayesian NetworkBayesian Network ModelConditional Probability TableValid RuleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
