Spinning quality prediction method based on firework algorithm improved BP neural network

Abstract

The invention discloses a spinning quality prediction method based on a firework algorithm improved BP neural network. According to the method, a firework algorithm is introduced into a BP neural network, and an optimizing mechanism of the firework algorithm is utilized to optimize a network weight and a threshold value of a BP neural network model; input and output indicators are selected, and an FWA-BP-based spinning quality prediction model is constructed; a standardized data set is utilized to perform learning and training on the FWA-BP-based spinning quality prediction model established in the step 2; and finally prediction of spinning quality is completed. Through the method, the problem that spinning quality is difficult to predict precisely due to numerous yarn quality influence factors in a spinning system and mutual coupling is solved, a function mapping relation between fiber indicators and resultant yarn quality can be effectively established, prediction of yarn quality in spinning production is realized, and the method is beneficial for increasing the quality management level of a spinning workshop.

Description

technical field [0001] The invention belongs to the technical field of spinning quality prediction and control, and relates to a spinning quality prediction method based on fireworks algorithm and improved BP neural network. Background technique [0002] The spinning system is in a complex environment where various factors such as high temperature, high humidity and high electromagnetic are intertwined with each other. There is a coupling relationship between the various factors. In addition, the spinning production and processing process is complex and the raw materials are frequently subjected to physical and chemical changes. The modification process makes the quality prediction in the textile production process more challenging compared with the traditional purely mechanical processing quality prediction. In particular, the fiber attribute index has grown geometrically, and has reached more than 300 at present. In addition, there are many factors affecting yarn quality i...

AI Technical Summary

Problems solved by technology

[0004] The object of the present invention is to provide a kind of spinning quality prediction method based on fireworks algorithm to improve BP neural network, which solves the problems of low prediction accuracy and high number of iterations in the training process of the existing neural network model

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