The Self-Organizing Maps: Background, Theories, Extensions and Applications

Yin, Hujun

doi:10.1007/978-3-540-78293-3_17

Cited by 142 publications

(115 citation statements)

References 113 publications

(148 reference statements)

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“…The self-organizing map (SOM) [5], also referred to as self-organizing feature map or Kohonen network, is a neural network that can be associated with vector quantization, visualization and clustering but can also be used as an approach for non-linear, implicit dimensionality reduction [17]. A SOM consists of a collection of neurons which are connected in a topological arrangement which is usually a two dimensional rectangular or hexagonal grid.…”

Section: Self-organizing Mapmentioning

confidence: 99%

Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps

Birgelen

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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Abstract. Modern Cyber-Physical Production Systems provide large amounts of data such as sensor and control signals or configuration parameters. The available data enables unsupervised, data-driven solutions for model-based anomaly detection and anomaly localization: models which represent the normal behavior of the system are learned from data. Then, live data from the system can be compared to the predictions of the model to detect anomalies and perform anomaly localization. In this paper we use self-organizing maps for the aforementioned tasks and evaluate the presented methods on real-world systems.

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Section: Self-organizing Mapmentioning

confidence: 99%

Anomaly Detection and Localization for Cyber-Physical Production Systems with Self-Organizing Maps

Birgelen

Niggemann

2018

IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency

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“…In both SOM algorithms the learning process is performed over a prescribed number of iterations that should lead to an asymptotic equilibrium. Even if Kohonen (2001) argued that convergence is not a problem in practice, the convergence of the learning process to an optimal solution is however an unsolved issue (convergence has been formally proved only for the univariate case, Yin, 2008). The reason is that, unlike other neural network techniques, a SOM does not perform a gradient descent along a cost function that has to be minimized (Yin, 2008).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Even if Kohonen (2001) argued that convergence is not a problem in practice, the convergence of the learning process to an optimal solution is however an unsolved issue (convergence has been formally proved only for the univariate case, Yin, 2008). The reason is that, unlike other neural network techniques, a SOM does not perform a gradient descent along a cost function that has to be minimized (Yin, 2008). Hence, in order to achieve reliable maps, the degree of optimality has to be assessed in other ways, e.g., by means of specific error met-rics.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Wave extreme characterization using self-organizing maps

et al. 2016

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Abstract. The self-organizing map (SOM) technique is considered and extended to assess the extremes of a multivariate sea wave climate at a site. The main purpose is to obtain a more complete representation of the sea states, including the most severe states that otherwise would be missed by a SOM. Indeed, it is commonly recognized, and herein confirmed, that a SOM is a good regressor of a sample if the frequency of events is high (e.g., for low/moderate sea states), while a SOM fails if the frequency is low (e.g., for the most severe sea states). Therefore, we have considered a trivariate wave climate (composed by significant wave height, mean wave period and mean wave direction) collected continuously at the Acqua Alta oceanographic tower (northern Adriatic Sea, Italy) during the period 1979-2008. Three different strategies derived by SOM have been tested in order to capture the most extreme events. The first contemplates a pre-processing of the input data set aimed at reducing redundancies; the second, based on the post-processing of SOM outputs, consists in a two-step SOM where the first step is applied to the original data set, and the second step is applied on the events exceeding a given threshold. A complete graphical representation of the outcomes of a two-step SOM is proposed. Results suggest that the post-processing strategy is more effective than the pre-processing one in order to represent the wave climate extremes. An application of the proposed two-step approach is also provided, showing that a proper representation of the extreme wave climate leads to enhanced quantification of, for instance, the alongshore component of the wave energy flux in shallow water. Finally, the third strategy focuses on the peaks of the storms.

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“…To circumvent this issue, self-organizing maps (SOMs), a class of neural-network algorithms (Kohonen, 2001), were also computed. These techniques are now increasingly used for data visualisation, clustering and classification of large datasets (Yin, 2008). They allow representation of a multidimensional dataset by nonlinear projection of artefacts in a lower dimension space, usually represented by discrete locations in a regular 2D lattice.…”

Section: Statistical Treatment Of Morphological Datamentioning

confidence: 99%

“…Despite the loss of linearity in the output space, the topological relationships between objects (the order of the distances) are preserved (Liu and Weisberg, 2005). In our case, the use of SOMs can provide a first, exploratory step for further clustering of large datasets (Yin, 2008). Artefacts are assigned to the most similar prototype (also called codebook) vectors, which represent a set of locations summarizing the original data.…”

Section: Statistical Treatment Of Morphological Datamentioning

confidence: 99%