利用CYK算法寻找模糊推理路径

Pragya Shukla, S. Tokekar, Suresh Jain
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引用次数: 1

摘要

利用Chandwani & Chaudhari[4]引入的模糊演绎图知识表示模型,提出了一种受CYK算法影响的寻找模糊推理路径的新算法[2]。在模糊演绎图(FDG)中,已经存在一种系统的基于Dijkstra最短路径框架的模糊推理路径(FRP)查找方法。在FDG中,边的权值在模糊区间[0-1]内为实数。在权值上求乘法的最大值,而不是在权值之和上求最小值[4]。我们的FRP算法是将CYK解析算法和FRP模糊推理算法结合在一起,生成模糊值最大的路径。CYK算法采用自底向上的方法,利用动态规划原理确定源节点到目标节点的FRP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Finding fuzzy reasoning path using CYK algorithm
Using the knowledge representation model, introduced by Chandwani & Chaudhari [4] known as fuzzy deduction graph, we present a new algorithm of finding fuzzy reasoning path which is influenced by CYK algorithm [2]. In a fuzzy deduction graph (FDG), a systematic method of finding the fuzzy reasoning path (FRP) already exists, which is based on Dijkstra's shortest path framework. In FDG the weights of edges are real numbers in the fuzzy interval [0–1]. The maximum of multiplication is obtained on weights instead of minimum of summation of weights [4]. Our FRP algorithm is conglomeration of CYK algorithm of parsing and FRP algorithm for fuzzy reasoning which generates the path with the greatest fuzzy value. CYK algorithm employs a bottom up approach with the principle of dynamic programming to determine the FRP from source node to the destination node.
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