2026-07-26T03:02:08Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/4870 2022-03-24T23:16:28Z 1582963302567:1597824273898 user-ucsy

Performance analysis of q-valued dimensions Parametric Vector Neural Network classifier Soe, Nwe Nwe Htay, Win Neural network ensemble techniques havebeen shown to be very accurate classificationtechniques. However, in some real-life applications,a number of classifiers required to achieve areasonable accuracy is enormously large and hencevery space consuming. This paper introduces specialneural method, Parametric Vector Neural Network(VNN), which has great associative memory and highperformance. Parametric VNN analyzed usingvarious size of database having randomly createdpatterns, noise levels, and fixed q-dimensions. Theresult shows that it has capacity much greater thanconventional Neural Networks. Once T matrix iscreated for the stored patterns in Database, mostsimilar pattern with the input one can be achievedeasily by just multiplying two matrices. The resultingassociative memory can recognize highly noisy andcorrelate input patterns. 2017-02-16 http://hdl.handle.net/20.500.12678/0000004870 https://meral.edu.mm/records/4870