Extension neural network-type 2 and its applications.
A supervised learning pattern classifier, called the extension neural network (ENN), has been described in a recent paper. In this sequel, the unsupervised learning pattern clustering sibling called the extension neural network type 2 (ENN-2) is proposed. This new neural network uses an extension distance (ED) to measure the similarity between data and the cluster center. It does not require an initial guess of the cluster center coordinates, nor of the initial number of clusters. The clustering process is controlled by a distanced parameter and by a novel extension distance. It shows the same capability as human memory systems to keep stability and plasticity characteristics at the same time, and it can produce meaningful weights after learning. Moreover, the structure of the proposed ENN-2 is simpler and the learning time is shorter than traditional neural networks. Experimental results from five different examples, including three benchmark data sets and two practical applications, verify the effectiveness and applicability of the proposed work.
Wang
Mang-Hui
MH
Institute of Information and Electrical Energy, National Chin-Yi Institute of Technology, Taichung, Taiwan, ROC. wangmh@chinyi.ncit.edu.tw
D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*