Self Organized Dynamic Tree Neural Network

Cluster analysis is a technique used in a variety of fields. There are currently various algorithms used for grouping elements that are based on different methods including partitional, hierarchical, density studies, probabilistic, etc. This article will present the SODTNN, which can perform clustering by integrating hierarchical and density-based methods. The network incorporates the behavior of self-organizing maps and does not specify the number of existing clusters in order to create the various groups

TopoART: A Topology Learning Hierarchical ART Network

Tscherepanow M. TopoART: A Topology Learning Hierarchical ART Network. In: Diamantaras K, Duch W, Iliadis LS, eds. Artificial Neural Networks (ICANN 2010). Lecture Notes in Computer Science, 6354. Berlin: Springer; 2010: 157-167.In this paper, a novel unsupervised neural network combining elements from Adaptive Resonance Theory and topology learning neural networks, in particular the Self-Organising Incremental Neural Network, is introduced. It enables stable on-line clustering of stationary and non-stationary input data. In addition, two representations reflecting different levels of detail are learnt simultaneously. Furthermore, the network is designed in such a way that its sensitivity to noise is diminished, which renders it suitable for the application to real-world problems

An Extended TopoART Network for the Stable On-Line Learning of Regression Functions

Tscherepanow M. An Extended TopoART Network for the Stable On-Line Learning of Regression Functions. In: Lu B-L, Zhang L, Kwok J, eds. Neural Information Processing : 18th International Conference, ICONIP 2011, November 13-17, 2011, Proceedings, Part II. Lecture notes in computer science, 7063. Berlin: Springer; 2011: 562-571.In this paper, a novel on-line regression method is presented. Due to its origins in Adaptive Resonance Theory neural networks, this method is particularly well-suited to problems requiring stable incremental learning. Its performance on five publicly available datasets is shown to be at least comparable to two established off-line methods. Furthermore, it exhibits considerable improvements in comparison to its closest supervised relative Fuzzy ARTMAP