Interval type index forecasting method based on Bayesian network and extreme learning machine

• Main Authors: LIU MIN, NING KEFENG, DONG MINGYU, WU CHENG
• Format: Patent
• Language: English
• Published: 22.04.2015
• Subjects: CALCULATING; COMPUTING; COUNTING; DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES; ELECTRIC DIGITAL DATA PROCESSING; PHYSICS; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR

The invention discloses an interval type index forecasting method based on a Bayesian network and an extreme learning machine, belongs to the fields of automatic control, information technologies and advanced manufacturing, and particularly relates to learning of parameters of an asymmetric Gaussian distribution Bayesian ELM model and adaptive adjustment on asymmetric weights. The interval type index forecasting method is characterized by including the following steps: as for the characteristic of the uncertainty of a complex production process, production indexes are described through the number of intervals, asymmetric Gaussian distribution serves as output distribution in an ELM model, the Bayesian ELM model with the weights is obtained, and the parameters of the Bayesian ELM model are learnt under an experience Bayesian frame through practical running data in the complex production process; on the basis, the pair of reciprocal weights are learnt with an adaptive adjustment method, and finally the forecasting value of the interval type indexes is obtained. By means of the interval type index forecasting method, the production indexes in the practical production process can be forecast, and the interval type index forecasting method can be used for guiding operation optimization and dynamic scheduling in the production process.