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

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.