Conference paperAuthors: Nong, Thi Hoa; Bui, The Duy (2012)
Unsupervised neural networks are known for their ability to cluster inputs into categories based on the similarity among inputs. Fuzzy Adaptive Resonance Theory (Fuzzy ART) is a kind of unsupervised neural networks that learns training data until satisfying a given need. In the learning process, weights of categories are changed to adapt to noisy inputs. In other words, learning process decides the quality of clustering. Thus, updating weights of categories is an important step of learning process. We propose a new effective learning rule for Fuzzy ART to improve clustering. Our learning rule modifies weights of categories based on the ratio of the input to the weight of chosen catego... 
![[doi 10.1109%2Ftaai.2012.60] Hoa, Nong Thi; Bui, The Duy -- [IEEE 2012 Conference on Technologies and Applications of Artificial Intelligence (TAAI) - Tainan, Taiwan (2012.11.16-2012.11.1.pdf.jpg](/retrieve/47ef2cb2-2409-4b55-b8ed-5ca243db18ad/%5bdoi%2010.1109%252Ftaai.2012.60%5d%20Hoa%2c%20Nong%20Thi%3b%20Bui%2c%20The%20Duy%20--%20%5bIEEE%202012%20Conference%20on%20Technologies%20and%20Applications%20of%20Artificial%20Intelligence%20%28TAAI%29%20-%20Tainan%2c%20Taiwan%20%282012.11.16-2012.11.1.pdf.jpg)