一种基于模式匹配的模糊模型及其神经网络实现(英)

通过模糊模式匹配概念，引入了一种新的模糊模型，并用神经网络方法对该模型进行辨识，以获得更高的精度．仿真实验表明，该模糊模型不仅简单易行，而且比通常的模糊模型有更强的辨识能力，另外，本文还讨论了模型的进一步求精，以及模糊神经网络的一般性问题．     

Real-time recognition of substring repetition and reversal

Real-time multitape Turing machine algorithms are presented for recognizing the languages {wxyxz^*:|w|=r|x,|z| =t|x|} and {wxyx ^R _z ^*:|w|=r|x,|z| =t|x|} for fixedr, s, andt and for string-matching with forced mismatches.This research was supported in part by the National Science Foundation under Grant MCS77-06613 (first author) and the Bat-Sheva Fund (second author). Part of the work was conducted while the second author was at the IBM Thomas J. Watson Research Center, Yorktown Heights, New York. A preliminary report was included in a paper the authors presented at the Seventeenth Annual IEEE Symposium on Foundations of Computer Science, Houston, Texas, October 1976.     

KMP算法与特殊子串滑动算法整合的构想与实践

在数据结构模式匹配算法中,Brute-Force算法是一个重要的基础算法,但是在性能上远不及KMP算法。而理解KMP及其改进算法是一个复杂的问题,其关键是KMP函数的求解过程。通过特殊子串滑动算法与KMP算法整合的实践,简化了特殊问题,省略了KMP函数的求解过程,在很大程度上提高了模式匹配问题的工作效率。