مشخصات پژوهش

عنوان	Multi-Level Fuzzy Min-Max Neural Network Classifier
نوع پژوهش	مقاله چاپ‌شده در مجلات علمی
کلیدواژه‌ها	Classification, fuzzy min-max, hyperbox, machine learning, neural networks, neurofuzzy, neuron, supervised learning.
چکیده	In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (θ), with a training accuracy of 100% in most cases.
پژوهشگران	رضا داوطلب (نفر اول)، میرحسین دزفولیان (نفر دوم)، محرم منصوری زاده (نفر سوم)