THE ANALYSIS ON REGIONAL DIFFERENTIATIONS OF SUSTAINABLE BY USING ARTIFICIAL NEURAL NETWORDS
使用自组织映射神经网络，不依赖参数假设，直接对区域社会经济系统模式进行映射和分类，将31个省份分为5类，结果与专家判断一致，为评估区域可持续性提供了新方法。
Artificial neural networks, originally inspired by their biological namesakes, are composed of many simple intercommunicating elements, or neurons, working in parallel to solve a problem. What makes them exciting is the fact that once a network has been set up, it can learn in self-organizing way that seems to mimic simple biological nervous systems. Because neural networks can be trained to respond in parallel to the inputs presented to them, they often are much faster than more conventional methods.In this paper, a soundly trained self-organizing map (SOFM), developed by Teuvo Kohonen in 1981, is employed to measure the sustainability of regional socioeconomic system. Without assuming parametric relationship, the neural network directly maps the patterns of socioeconomic system. When the neural network is trained appropriately, it classifies the data sets.The run results of SOFM show that 31provinces(cities) or autonomous regions are classified into 5 groups, which are in of accord with experts. The results also indicate that ANN is an alternative approach of assessing regional sustainability.