基于RBF神经网络的手绘电气草图识别研究-计算机应用技术专业论文.docxVIP

器均采用 RBF 神经网络。第一级分类器用作预分类，第二级分类器用作细分类。 预分类采用一个 RBF 神经网络，用一级特征作为预分类的输入特征向量；细分 类采用三个 RBF 神经网络，用二级特征作为细分类的输入特征向量。并通过实 验验证了这种分类系统的有效性。 关键词：手绘电气草图在线识别；特征提取和选择；RBF 神经网络；分类器； 聚类  Abstract Due to the advantage of simple, fast and conveniently storage, CAD technology plays an irreplaceable role in various fields designing. It can greatly enhance the qua l- ity of design, reduce the design cycle, share device resources and s trengthen data han- dling capacity. But CAD technology is mainly applied in early stages of design and has no role in catching inspiration and thinking exploration. It can’t meet the needs of early stage designing. But paper sketches have their own disadvantage too. It lack of “designed memory”; hard to storage, arrange, search and reuse, especially lack of va- lid capability of interaction and alternation. For this reason, to study a sort of design tool that can combine paper-and-pencil hand-drawn sketch with computer is the hope of designers and is of great significance. Electric circuit diagram design is an impor- tant field of CAD technology. The thesis pointed against the defects of CAD technology to study thoroughly on- line recognition of hand-drawn electronic component symbol and make research and experiments on the key component of on- line hand-drawn electronic component symbol. The main part works as follows: 1. A two levels hand-drawn electronic component symbol feature selection and extraction method that aimed at RBF(Radial Basis Function) neural networks is stu- died in this thesis. Structural feature and relationship feature of hand-drawn electronic component symbol are defined in this thesis. Moreover, the definitions are applied in the feature extraction of hand-drawn electronic component symbol. Feature extraction and selection is the most crucial element in recognition of hand-drawn electronic component symbol, which affects directly recognition effect. This thesis used RBF neural networks which have the best approximation capability