Iterative updating-based UKFNN aluminum electrolysis power consumption model construction method

Abstract

The invention discloses an iterative updating-based UKFNN aluminum electrolysis power consumption model construction method. The method comprises the following steps of initializing structural parameters of an aluminum electrolysis power consumption model; calculating a group of sampling points of the power consumption model; predicting a state and a covariance of the power consumption model at next moment by a state equation F according to a state estimation value of the power consumption model at previous moment; re-sampling sampling points generated by taking a predicted state value as a center and a predicted variance as a covariance, updating the sampling points and the covariance by using an observation function, and performing covariance updating of a state variable and an observation value; and re-performing state estimation updating by using the updated state at the next moment and covariance matrix at the next moment. According to the method, an iterative method is combined with UKFNN so as to obtain a more accurate estimation value.

Description

technical field [0001] The present invention relates to the technical field of digital computing equipment or data processing equipment or data processing methods that are especially suitable for specific functions, and in particular to a method for constructing a UKFNN aluminum electrolysis power consumption model based on iterative updates. Background technique [0002] After years of rapid development, China has gradually developed into a major country in aluminum metallurgy. According to the statistics of the energy cost of aluminum metallurgy, the mining of bauxite is more than one-third of the cost of primary aluminum, and the consumption of aluminum per ton is 4.5 times the energy consumption of steel per ton, with an average energy consumption of about 182-212mj / kg. The process accounts for about 64%, and the requirement for energy saving is becoming increasingly strong. The application of energy saving technology in aluminum electrolysis occupies an important positio...

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Problems solved by technology

However, UKFNN still has the defects of the Kalman filter itself. During the transfer process of the covariance matrix, the state covariance matrix loses symmetry due to unstable calculations, which makes the UKFNN algorithm invalid; use linear least mean square estimation to measure The accuracy of the update is not high

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