基于可拓表征与神经网络的苗族图案设计

针对苗族图案的文化传承及设计应用问题,提出基于可拓表征和神经网络的民族图案创新设计方法,对苗族蜡染图案进行解构、映射和重构.首先对苗族蜡染图案进行可拓表征,运用发散树法构建设计生长阶段模型对苗族图案基元进行拓展分析.其次基于感性工学对苗族蜡染图案进行感性意象分析,提出一种面向图案构型、纹样语义和种类的图案解构方法,构建图案特征要素解构空间和情感意象认知空间.运用神经网络构建感性预测模型根据用户意象偏好推荐图案构型等设计要素,对设计思维进行收敛,并与线性回归预测模型进行对比验证其优势性.最后根据神经网络感性预测模型推荐的特征要素应用形状文法对苗族蜡染图案进行细化设计.以苗族蜡染图案为例,验证该方法的可行性,为其他民族图案的解构及创新设计提供参考.

Miao Pattern Design Based on Extensional Representation and Neural Network

With regard to the cultural inheritance and design application of Miao patterns, an innovative design method of ethnic patterns based on extensional representation and neural network is proposed to deconstruct, map, and reconstruct Miao batik patterns. Firstly, Miao batik patterns are characterized by extension, and the divergence tree is used to construct the design growth stage model to expand and analyze the elements of Miao patterns. Secondly, Miao batik patterns are analyzed with Kansei images based on Kansei engineering, proposing a pattern deconstruction method for pattern configuration, pattern semantics and types, with which the deconstructive space of pattern feature elements and the cognitive space of emotional images are constructed. The neural network is used to construct the Kansei prediction model that recommends design elements such as pattern configuration to users in terms of their preference, and the design thinking is converging. Its advantages are verified by the comparison with the linear regression prediction model. Finally, shape grammar is used to refine Miao batik patterns according to the characteristic elements recommended by the model. The method is verified feasible with Miao batik patterns and can provide a reference for the deconstruction and innovative design of other ethnic patterns.