Improved ELM algorithm-based capillary quality forecasting method

Abstract

The invention relates to an improved ELM algorithm-based capillary quality forecasting method. The method comprises the steps of collecting a plurality of sets of historical field data during the capillary perforation process so as to construct a training set; according to collected field data, determining the input layer, the output layer and the hidden layer of an integrated ELM network; in combination with a plurality of common excitation functions, determining an excitation function of the integrated ELM network in the weight setting manner; optimizing each weight in the excitation function of the integrated ELM network by adopting a genetic algorithm, and obtaining an optimal excitation function; adopting the training set for training the integrated ELM network, and completing the construction of the integrated ELM network; inputting data obtained during the actual production process into each sub-network of the integrated ELM network, and obtaining the output result of each sub-network so as to obtain the output prediction result of the integrated ELM network, namely the quality prediction result of a capillary tube. According to the invention, the rapid performance of an ELM model and the robustness of an integration method are inherited. As a result, the quality of the capillary tube can be forecasted more accurately.

Description

technical field [0001] The invention belongs to the quality prediction technology in the field of regression technology, and in particular relates to a capillary quality prediction method based on an improved ELM algorithm. Background technique [0002] As the first process of seamless steel pipe production, piercing has a very important impact on the quality of steel pipes; the quality problems caused by the piercing process will not be alleviated in the subsequent process, but will cause more serious quality problems of steel pipes; so , the establishment of a capillary quality prediction model using the collected perforation process data has very important guiding significance for the steel rolling process; common prediction methods are mainly based on time series method, Kalman filter method, neural network, support vector machine (SVM), etc.; The prediction results of the time series method are unstable, and its model parameters are difficult to determine; the neural ne...

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Problems solved by technology

[0005] Aiming at the shortcomings of the capillary quality prediction method in the prior art that the sample data has noise interference and the prediction result of the ELM model is u

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