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Performance analysis of granular computing model based on Fuzzy based linear programming problem

机译:基于模糊线性规划问题的粒度计算模型性能分析

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Granular computing is not only a computing model for computer centered problem solving, but also a thinking model for human centered problem solving. In this paper we have discussed the architecture of granular computing models, strategies, and applications. Especially, comparison on the perspectives of granular computing in various aspects as AI, data mining and phases of software engineering are presented, including requirement specification, system analysis and design, algorithm design, structured programming, software testing. Here we have discovered the mining patterns in the sequence of events has been an area of active research in AI. However, the focus in this body of work is on discovering the rule underlying the generation of a given sequence in order to be able to predict a plausible sequence continuation(the rule to predict what number will come next, given a sequence of numbers). Here we have used the Fuzzy based linear programming problem of Granular computing model for the purpose of mathematical simulation and we have compared it with the different existing algorithms for better performance analysis.
机译:粒度计算不仅是用于以计算机为中心的问题解决的计算模型,还是用于以人为中心的问题解决的思维模型。在本文中,我们讨论了粒度计算模型,策略和应用程序的体系结构。特别是,从AI,数据挖掘和软件工程的各个阶段对粒度计算的观点进行了比较,包括需求规范,系统分析和设计,算法设计,结构化编程,软件测试。在这里,我们发现了事件序列中的挖掘模式一直是AI研究中的一个活跃领域。但是,此工作的重点是发现给定序列的生成所基于的规则,以便能够预测合理的序列连续性(给定数字序列,该规则可以预测接下来将要出现的数字)。在这里,出于数学模拟的目的,我们已经使用了基于模糊的粒度计算模型的线性规划问题,并将其与现有的不同算法进行了比较,以进行更好的性能分析。

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