Open AccessApplication of regression neural network and MIV algorithm in visual communication design
Author(s) -
Zhixun Wen
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1941/1/012081
Subject(s) - artificial neural network , robustness (evolution) , generalization , computer science , regression , linear regression , regression analysis , nonlinear system , artificial intelligence , sample (material) , nonlinear regression , value (mathematics) , algorithm , data mining , machine learning , statistics , mathematics , mathematical analysis , biochemistry , physics , chromatography , quantum mechanics , gene , chemistry
Regression neural network algorithm can continuously learn the characteristics of training samples, and it has good data tolerance, robustness and generalization ability, and has been widely used in all kinds of nonlinear regression problems. In this paper, a multi-input and single output neural network regression model is constructed. Through the weight training of a large number of experimental sample data characteristics, the nonlinear regression mapping relationship of multi parameter input is established, so as to realize the accurate prediction of the basic situation of cultural and creative enterprises, then to analyze the influence of cultural and creative enterprises on the visual communication design course. The result of the correlation coefficient of the two is 0.79. The average relative errors between the predicted results and the actual results are 9.167% and 9.63%, respectively, both of which are less than 12%. It shows that the regression neural network model constructed in this paper can accurately analyse and predict the data, and the error between the predicted value and the target output value of the network model meets the actual requirements. So it has good prediction accuracy and generalization performance.