基于表格查寻学习算法的自适应模糊分类器

Adaptive fuzzy classifier based on table-looking learning algorithm

以手写体数字识别问题为背景,提出了一种基于表格查寻学习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,该自适应模 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。

An improved adaptive fuzzy system based on table-looking learning algorithm,adaptive fuzzy classifier was developed.For the pattern recognition problems,simulation studies were 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 better performance in recognition ability,incorporationg the linguistic information and comptational simplicity.All these show the superiority and potential of adaptive fuzzy techniques in solving pattern recognition problems.