Titlebook: Advances in Self-Organizing Maps; 7th International Wo José C. Príncipe,Risto Miikkulainen Conference proceedings 2009 Springer-Verlag Berl

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https://doi.org/10.1007/978-94-011-3692-1 recursive temporal context of Merge Neural Gas (MNG) with the incremental Growing Neural Gas (GNG) and enables thereby the analysis of unbounded and possibly infinite time series in an online manner. There is no need to define the number of neurons a priori and only constant parameters are used. In

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Skewness and Kurtosis of Safety Marginin computing decision thresholds from the distribution of quantization errors produced by normal training data. These thresholds are then used for classifying incoming data samples as normal/abnormal. For this purpose, we carry out performance comparisons among five competitive neural networks (SOM,

CAMEO

Computational Stochastic Mechanicss. The goal is to ensure a proper operation of the engines, in all conditions, with a zero probability of failure, while taking into account aging. The fact that the same engine is sometimes used on several aircrafts has to be taken into account too..The maintenance can be improved if an efficient p

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Computational Stochastic Programmingshort term memory structure called Gamma memory. The proposed model allows increasing depth without losing resolution, by adding more contexts. When using a single stage of the Gamma filter, the Merge SOM model is recovered. The temporal quantization error is used as a performance measure. Simulatio

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Springer Optimization and Its Applicationslow-dimensional output space. Most swarm methods are derivatives of the Ant Colony Clustering (ACC) approach proposed by Lumer and Faieta. Compared to clustering on Emergent Self-Organizing Maps (ESOM) these methods usually perform poorly in terms of topographic mapping and cluster formation. A unif