ANN model of subdivision error based on genetic algorithm

According to the test data of subdivision errors in the measuring cycle of angular measuring system, the characteristics of subdivision errors generated by this system are analyzed. It is found that the subdivision errors are mainly due to the rotary-type inductosyn itself. For the characteristic of cyclical change, the subdivision errors in other measuring cycles can be compensated by the subdivision error model in one measuring cycle. Using the measured error data as training samples, combining GA and BP algorithm, an ANN model of subdivision error is designed. Simulation results indicate that GA reduces the uncertainty in the training process of the ANN model, and enhances the generalization of the model. Compared with the error model based on the least-mean-squared method, the designed ANN model of subdivision errors can achieve higher compensating precision.

面向地理对象多尺度表达的剖分编码方法研究

现有模型对影像数据中的地理对象进行多尺度表达很困难,鉴于全球剖分模型在对地理对象表达时所具备的全球唯一性、层次性、多尺度性、编码具有地学含义等特点,提出了面向地理对象多尺度表达的剖分编码方法,目的是对地理对象赋予统一的剖分编码,使编码具有地学含义和多尺度特性。实验证明,面向地理对象多尺度表达的剖分编码方法可以满足地理对象多尺度剖分化表达的需要,为新一代GIS解决地理信息"尺度鸿沟"问题,向用户提供"连续"、"自适应"的表达尺度奠定了基础。