The Self-Organizing Map (SOM), with its variants, is the most popular artificial neural network algorithm in the unsupervised learning category. About 4000 research articles on it have appeared in the open literature, and many industrial projects use the SOM as a tool for solving hard real world problems. Many fields of science have adopted the SOM as a standard analytical tool: statistics, signal processing, control theory, financial analyses, experimental physics, chemistry and medicine. This new edition includes a survey of over 2000 contemporary studies to cover the newest results. Case examples are provided with detailed formulae, illustrations, and tables. Further, a new chapter on software tools for SOM has been included whilst other chapters have been extended and reorganised.

Inhaltsangabe1. Mathematical Preliminaries.- 2. Neural Modeling.- 3. The Basic SOM.- 4. Physiological Interpretation of SOM.- 5. Variants of SOM.- 6. Learning Vector Quantization.- 7. Applications.- 8. Software Tools for SOM.- 9. Hardware for SOM.- 10. An Overview of SOM Literature.- 11. Glossary of "Neural" Terms.- References.

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