Evolving Fuzzy Modeling for MANETs Using Lightweight Online Unsupervised Learning
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The study presents a methodology for evolving fuzzy modeling tasks in Mobile Ad hoc Networks (MANETs) based on distributed data-driven fuzzy clustering and reasoning. The fuzzy clustering is exploited for the purpose of learning fuzzy inference rules online. That calls for one-pass Lightweight Evolving Fuzzy Clustering Method (LEFCM) suitable for deploying on mobile devices with constrained resources in MANETs. There is no standard method to determine the optimal number of fuzzy rules and most of the fuzzy systems still apply the trial and error method, unsuitable for online modeling tasks. The proposed methodology addresses the issues of uncertainties, simplicity and speed to run in non-intrusive way. It estimates online the number of clusters and their centers in the input data space, accordingly the fuzzy rules, by online adaptation of the LEFCM threshold value that affects the number of clusters. Adaptation is based on the combination of geometrical and statistical analyses, as well as on incorporating a multidimensional fuzzy membership degree into the clustering process. The proposed LEFCM is proven by using traditional cluster validity indexes and tested on real data sets.
KeywordsEvolving systems Data-driven fuzzy models Online evolving clustering Mobile Ad hoc networks Unsupervised learning Knowledge and data integration
The research has been supported by DFG grants N 436BUL112/08. Also thanks goes to Prof. Peter Martini, Nils Aschenbruck, Elmar Gerhards-Padilla from Inst. of Computer Science IV, Bonn University for their strong support and provided data from a realistic disaster area scenario.
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