A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Neuroengineering of Clustering Algorithms

Cluster analysis can be broadly divided into multivariate data visualization, clustering algorithms, and cluster validation. This dissertation contributes neural network-based techniques to perform all three unsupervised learning tasks. Particularly, the first paper provides a comprehensive review on adaptive resonance theory (ART) models for engineering applications and provides context for the four subsequent papers. These papers are devoted to enhancements of ART-based clustering algorithms from (a) a practical perspective by exploiting the visual assessment of cluster tendency (VAT) sorting algorithm as a preprocessor for ART offline training, thus mitigating ordering effects; and (b) an engineering perspective by designing a family of multi-criteria ART models: dual vigilance fuzzy ART and distributed dual vigilance fuzzy ART (both of which are capable of detecting complex cluster structures), merge ART (aggregates partitions and lessens ordering effects in online learning), and cluster validity index vigilance in fuzzy ART (features a robust vigilance parameter selection and alleviates ordering effects in offline learning). The sixth paper consists of enhancements to data visualization using self-organizing maps (SOMs) by depicting in the reduced dimension and topology-preserving SOM grid information-theoretic similarity measures between neighboring neurons. This visualization\u27s parameters are estimated using samples selected via a single-linkage procedure, thereby generating heatmaps that portray more homogeneous within-cluster similarities and crisper between-cluster boundaries. The seventh paper presents incremental cluster validity indices (iCVIs) realized by (a) incorporating existing formulations of online computations for clusters\u27 descriptors, or (b) modifying an existing ART-based model and incrementally updating local density counts between prototypes. Moreover, this last paper provides the first comprehensive comparison of iCVIs in the computational intelligence literature --Abstract, page iv

顔の表情に基づいた感情と行動を表出するシステムに関する研究

九州工業大学博士学位論文 学位記番号:情工博甲第320号 学位授与年月日:平成29年3月24日1 Introduction|2 Configuration of CONBE Robot System|3 Animal-like Behavior of CONBE Robot using CBA|4 Emotion Generating System of CONBE Robot|5 Experiment and discussion|6 Conclusions九州工業大学平成28年

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