摘要： 以手写体数字识别问题为背景，提出了一种基于最近邻聚类算法的自适应模糊分类器，并用Matlab 给出了自适应模糊分类器的实现，进而对其进行了仿真。仿真结果表明，所提出的自适应模糊分类器在手写体数字识别的识别性能、利用语言信息、计算复杂性等方面均优于采用BP 算法的三层前馈分类器，体现了自适应模糊处理技术用于模式识别的优越性和潜力。

关键词: 手写体数字识别；自适应模糊分类器；人工神经网络

Abstract: An improved adaptive fuzzy system based on nearest neighborhood clustering algorithm——adaptive fuzzy classifier, for the pattern recognition problems is developed.Simulation studies are made by applying the adaptive fuzzy classifier and the BP 3-layer feed-forward neural network classifier to the handwritten digit recognition problems. As compared to the BP neural network, the adaptive fuzzy classifier has two significant advantages: 1) computational simplicity; 2) easiness of incorporating the expert’s linguistic information. All these show the superiority and potential of adaptive fuzzy techniques in solving pattern recognition problems

Key words: Handwritten digit recognition; Adaptive fuzzy classifier; Artificial neural network