期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2011
卷号：2
期号：4Ver1
出版社：Ayushmaan Technologies
摘要：This paper focuses on the developing of neural network and pattern recognization, using adaptive reasoning theory and hebbian learning rules, with the objective of classification accuracy of the network on unseen test data. In ART when a training vector is inserted, a Hebbian rule is chosen to be trained through the network in order to make algorithm real time predictions of network traffic. There is considerable physiological evidence that a Hebb-like learning rule applies to the strengthening of synaptic efficacy. Hebbian learning can strengthen the neural response that is elicited by an input; this can be useful if the response made is appropriate to the situation, but it can also be counterproductive if a different response would be more appropriate. From a computational point of view, Hebbian learning can certainly lead one in the wrong direction, and some form of control over this is necessary. Also, experimental findings clearly show that learning can be affected by accuracy or outcome feedback.multi-t
