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Development for granular computing-based multi-agent system for data fusion process

In data fusion systems, the characteristics of the information from the sensors include diversity, complexity and uncertainty. In this paper, the data fusion of the granular computing-based multi-layer structure is studied. Neural network and fuzzy system are adopted for the inference mechanism to construct an equivalent fuzzy logic system. Neural network clustering is used to cluster the concept lattices with different formal contexts. And in each concept lattice, fuzzy clustering is used to cluster the formal contexts. The design of the data fusion middleware in the multi-agent system MAS enables the two-step data fusion. This design is used to solve the issues caused by the imprecise, incomplete, fuzzy or contradictory inference. Simulation results on the fire detection in the intelligent building environment show the effectiveness and the feasibility of this design.