Networks Data Transfer Classification Based On Neural Networks

Data transmission classification is an important issue in networks communications, since the data process has ultimate impact organizing and arranging it according to size area prepare for minimize bandwidth enhancing bit rate. There are several methods mechanisms classifying transmitted type of efficiency. One most recent artificial neural (ANN). It considered one dynamic up-to-date research areas application. ANN a branch intelligence (AI). The network trained by backpropagation algorithm. Various combinations functions their effect while utilizing as file, classifier was studied validity these different types datasets analyzed. Back propagation university (BPNN) supported with Levenberg Marqurdte (LM) activation function might be utilized successful tool suitable set training learning which operates, when probability maximum. Whenever maximum likelihood method compared method, BPNN further accurate than method. A high predictive ability against stable well-functioning possible. Multilayer feed-forward algorithm also used classification. However proves more effective other algorithms.