Improving signal detection accuracy at FC of a CRN using machine learning and fuzzy rules

<span>The performance of a cognitive radio network (CRN) mainly depends on the faithful signal detection at fusion center (FC). In this paper, concept weighted Fuzzy rule in Iris data classification, as well as, four machine learning techniques named fuzzy inference system (FIS), <em>c</em>-means clustering (FCMC), support vector (SVM) and convolutional neural (CNN) are applied FC taking signal-to-interference plus noise ratio secondary users parameter. The gave accuracy 86.6%, which resembles energy model majority FC; however, CNN an 91.3% expense more decision time. FIS, FCMC SVM some intermediate results; combined method best result compared to that any individual technique.</span>