Survey on: Fuzzy Verdict Reveal on Predefined Neural Network

In this paper, a supervised learning method, called as a multi-level fuzzy min-max neural network classifier (MLF), is described. MLF utilizes fundamental ideas of the fuzzy min-max (FMM) method in a multi-level structure to arrange patterns. This method uses separate classifiers with littler hyperboxes in distinctive levels to group the specimens that are found in overlapping areas. The last outcome of the network is shaped by consolidating the outcomes of these classifiers. MLF is fit for learning nonlinear limits with a solitary pass through the information. As per the acquired results, the MLF method, contrasted with the other FMM networks, has the most elevated execution and the least affectability to maximum size of the hyperbox parameter (), with the best preparation precision as in most of the cases.