@inproceedings{d2550a0a524047219196ae101edb3a7c,
title = "Material texture image model optimization and result analysis",
abstract = "Targeted in the quantitative description of the relationship between the material's subjective features and objective parameters, this study builds a mathematical model by BP neural network. Then optimization of the thresholds and weights of BP material texture models is conducted to refine the accuracy and description ability of this network. Through the analysis of the result of GA-BP Model, the foundation established by the summary of relationship between the texture image and the objective material parameters can be used to forecast the emotional characters of the materials whose objective parameters are previously understood.",
keywords = "Genetic algorithm, Kansei engineering, Material texture, Neural network",
author = "Yan Zhou and Chen, \{Lu Wei\} and Tang, \{Ruo Yue\}",
year = "2014",
doi = "10.4028/www.scientific.net/AMM.457-458.1122",
language = "英语",
isbn = "9783037859247",
series = "Applied Mechanics and Materials",
pages = "1122--1125",
booktitle = "Frontiers of Mechanical Engineering and Materials Engineering II",
note = "2013 2nd International Conference on Frontiers of Mechanical Engineering and Materials Engineering, MEME 2013 ; Conference date: 12-10-2013 Through 13-10-2013",
}