INTERVAL TYPE INDICATOR FORECASTING METHOD BASED ON BAYESIAN NETWORK AND EXTREME LEARNING MACHINE

摘要

An interval type indicator forecasting method based on a Bayesian network and an extreme learning machine, which relates 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 of asymmetric weights. The method is characterized by comprising the following steps: as for the characteristic of the uncertainty of a complex production process, describing production indicators by using interval numbers; using asymmetric Gaussian distribution as output distribution in an ELM model, and acquiring the Bayesian ELM model having the weights; and learning parameters of the Bayesian ELM model under an experience Bayesian frame by using actual running data in the complex production process; on the basis, learning a pair of reciprocal weights by using an adaptive adjustment method; and finally, acquiring a forecast value of the interval type indicators. By means of the interval type indicator forecasting method, production indicators in the practical production process can be forecast, and the interval type indicator forecasting method can be used for guiding operation optimization and dynamic scheduling in the production process.