地理信息知识获取Rough-NN模型研究

Study on Rough-NN Model for Geographic Information Knowledge Discovery

提出了一种粗糙集结合神经网络的粗糙集神经网络模型,对具有高度自相关性的地理信息进行知识获取.主要思想是利用辨别矩阵形成约简算法,得到最简的if-then规则;然后构造三层神经网络模拟最简规则,其中网络的输入输出由本文提出的参数训练方法确定.本文利用VB实现该模型,并对松花江流域的洪涝干旱灾情进行了仿真实验,结果表明该模型可以快速地获取最简的if then规则,得到正确的决策结果.*

This paper presents a rough neural network (rough NN) model which is based on rough set theory and neural network technology to discover knowledge from geographic information that has high spatial autocorrelation. The main idea of this paper is to get the most concise if then rules by discernibility matrix. And a three layer neural network to simulate the most concise rules is constructed. Inputs and outputs of the neural network are decided by the parameter training method that is provided in this paper. This paper realizes the model with VB and presents a simulation of its use for judging drought and flood disasters in Songhua river basin. The results show that the model can quickly form the most concise rules and make right decision.