基于DFNN的语义驱动色彩智能设计研究

色彩是表达设计情感的重要手段，进行了色彩语义的量化处理，通过抽取模糊规则获得主要影响因素，根据色彩方案多维小样本的特点，提出了基于广义径向基函数（RBF）的动态模糊神经网络（DFNN）方法，智能模拟色彩方案的设计、选择过程。根据训练数据开发了色彩智能设计原型系统，结合汽车色彩智能设计进行了可行性验证，能够在较高层次上辅助设计师进行色彩设计。

Research on semantic-driven intelligent color design based on DFNN

Color design is one of the important means through which the designers express their emotion. This paper quan-tifies the expressions of color semantics, and obtains the main factors by extracting the fuzzy rules. Considering those scarce multi-dimensional training samples, this paper constructs a Dynamic Fuzzy Neural Network（DFNN）which is based on the generalized Radial Basis Function（RBF）as the neuron activation functions to intelligently simulate the process of designing and choosing a color scheme. After training those data samples, this paper verifies the feasibility of this method by developing an intelligent color design prototype system, which can intelligently assist the designers to design the color of cars with great efficiency at a high-level.