Abstract: The present disclosure relates to the technical field of campus local area networks (LANs), and particularly discloses a method and system for implementing multi-service bearer in a passive optical LAN (POL). The method includes: step S1: constructing a POL, and accessing an entire campus network at a bandwidth of Gigabit according to a point-to-multipoint star topology including three layers: a core layer, a convergence layer, and an access layer, to form a 10 Gbit backbone, wherein an optical network terminal enters a room and is deployed according to such a manner that one classroom or functional room has one terminal mode; step S2: planning and managing the entire POL, defining a plurality of LANs through software definition (SD-LAN), wherein different LANs bear different services; and step S3: allocating different service bandwidths to different LANs through a sharding mechanism of the PON, and the like.

Abstract: The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached.

Abstract: The present invention relates to a clustering method based on iterations of neural networks, which comprises the following steps: step 1, initializing parameters of an extreme learning machine; step 2, randomly choosing samples of which number is equal to the number of clusters, each sample representing one cluster, forming an initial exemplar set and training the extreme learning machine; step 3, using current extreme learning machine to cluster samples, which generates a clustering result; step 4, choosing multiple samples from each cluster as exemplars for the cluster according to a rule; step 5, retraining the extreme learning machine by using the exemplars for each cluster obtained from step 4; and step 6, going back to step 3 to do iteration, otherwise obtaining and outputting clustering result until clustering result is steady or a maximal limit of the number of iterations is reached.