Compressor outlet parameter prediction modeling method based on width learning system

Abstract

The invention discloses a compressor outlet parameter prediction modeling method based on a width learning system. The method comprises the steps of collecting data and preprocessing the data; constructing an outlet parameter prediction model of the compressor by utilizing a width learning system, setting the number of characteristic node groups of the width learning system as n, the number of k characteristic nodes in each group, the number of enhanced node groups as m and q enhanced nodes in each group, and performing modeling; when a new training data input model exists, using an incremental learning algorithm for carrying out online updating on an original model, an input data matrix of new training data being set as Xa belonging to Ra * M, an output data matrix being set as Ya belonging to Ra * C, a representing the number of newly-added training data, and performing model updating; and verifying the constructed model by using the root-mean-square error. According to the method, the performance prediction model of the large compressor can be quickly established, and the development time and cost of the model can be effectively saved.

Description

technical field [0001] The invention belongs to the technical field of industrial process modeling, and in particular relates to a compressor outlet parameter prediction modeling method based on a width learning system. Background technique [0002] Compressors are widely used in various industrial sectors due to their high operating efficiency and wide application range. They are important equipment in many complex industrial processes, and their safe and stable operation is crucial to the entire system. However, in the actual use process, there are problems such as difficult to accurately predict performance and unsatisfactory actual operation control effect, and consume a lot of energy and money. Therefore, the design and control of the compressor should follow the principles of energy saving and economy. However, an accurate performance prediction model is the basis for compressor design and control. Therefore, it is of great significance to establish an accurate compr...

AI Technical Summary

Problems solved by technology

Although deep learning has a powerful learning ability, its complex structure leads to more complicated parameters involved, resulting in a long training process to find a suitable model, which greatly reduces the efficiency of the system, and in order to obtain better learning effects , requires the support of a large number of high-performance computers, resulting in a substantial increase in cost

Images