Variable Discretization Precision Rough Logic Neural Network Based on Approximation Area Partition and Its Application to Remote Sensing Image Classification
基于近似域划分的可变离散精度粗逻辑网络及其遥感图像分类应用

A variable discretization precision rough logic neural network is proposed to solve contradiction between network precision and the size of network as well as generalization ability. Based on the approximation area partition,the universe discussed can be partitioned into certain area and possibility area. The important reason of misclassification is the granularity of the possibility area is too coarse. In this work,only possibility area is refined and the precision of the rough logic neural network is improved while the size of network is restrained. In the experiment of the remote sensing image classification about Changbai mountain area,the performance of conventional method is best when the discretization level is 7. The most approximated result is acquired,while less network cost and training time are expended,when this method is used.