Improving the Fuzzy Min-Max Neural Network with a K-nearest Hyperbox Expansion Rule for Pattern Classification

• المؤلفون الرئيسيون: Mohammed, Mohammed Falah, Chee, Peng Lim
• التنسيق: مقال
• اللغة: English
• منشور في: Elsevier Ltd 2017
• الموضوعات: QA76 Computer software
• الوصول للمادة أونلاين: http://umpir.ump.edu.my/id/eprint/16440/1/fskkp-2017-falah-Improving%20the%20fuzzy%20min-max1.pdf; http://umpir.ump.edu.my/id/eprint/16440/; https://doi.org/10.1016/j.asoc.2016.12.001
• المؤسسة: Universiti Malaysia Pahang Al-Sultan Abdullah

An improved Fuzzy Min-Max (FMM) neural network with a K-nearest hyperbox expansion rule is proposed in this paper. The aim is to reduce the FMM network complexity for undertaking pattern classification tasks. In the proposed model, a useful modification to overcome a number of identified limitations...