从神经网络中抽取土地评价模糊规则

为了明确土地评价中所训练神经网络的含义，使土地评价工作者可轻松地理解、判断所得到土地评价模型的正确性和合理性，提出从神经网络中抽取土地评价模糊规则的方法。现有的大多数从神经网络中提取方法，神经网络的输入属性要么局限于连续的，要么只适应于离散的，而土地评价因子往往既包含连续的又包含离散的、标称的，该文首先提出了一种输入属性值适应于这三种类型数据的模糊神经网络建立方法，进而给出一种从建立的神经网络中抽取其中较主要模糊规则的算法。试验表明，所提出的土地评价方法，可直接从样本中学习评价规律，使土地评价工作者易于理解，当出现抽取的规则与实际情况不吻合时，可重新训练神经网络和抽取规则，所得到的评价结果比BP网络的评价结果更准确，从而提高了土地评价的准确性。

Extracting fuzzy rules from neural networks for land evaluation

In order to make the land evaluation knowledge captured by neural networks transparent, and to make it easy for workers to judge the validity of the land evaluation mode, a method for extracting land evaluation fuzzy rules from a trained neural network was proposed in this paper. Current rule extraction approaches can deal with problems only with discrete-valued inputs or only with continuous-valued inputs. But, the land evaluation factors often contain the continuous-valued, discrete-valued and nominal-valued attributes, a method for constructing neural network with the three kinds of input attributes was proposed firstly. Moreover, an algorithm was developed to extract several main fuzzy rules for each output neuron. The results of experiment illuminate that the proposed method could be utilized to obtain the land evaluation rules from samples that were understandable representation for the users. When the extracted rules does not accord with the fact, the neural networks can be retrained and the rules can be extracted from it again. The proposed method can be more effective to evaluate land than BP neural networks. The validity of land evaluation is improved.