Abstract:

As for the problem of cognitive learning on distributed networks and complicated topological structures, the paper proposes a dynamic,information granularitybased and connectivitybased cognitive optimized  learning. The information granules of every network node hold the integrity of information expression under the condition of high degree of polymerization. Selfassembly polymerization of the nodes in the knowledge system and the strong connectivity between every two nodes are the kernel  model in the optimized learning result. The concept of polymerization degree of information granules and the connectivity among information granules, the static reduction of the evolution of the information granules which imitate the  cognitive learning process,and the dynamic imitation of connectivity intensity evolution which correspondes cognitive learning are used here. And the two processes accomplish a whole imitating cognition and reduction expression to every inputting sample in the learning system. This thesis aims to take the distributed topological structure as a theoretical model to propose the cognitive optimization rules for the information granularity of every node as well as the information procession and transmission among the nodes.

Key words: distributed network, information granules, connectivity, convergence, topological structure