Kohonen network

Kohonen, Teuvo; Honkela, Timo

doi:10.4249/scholarpedia.1568

Cited by 190 publications

(95 citation statements)

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“…It is essentially a special type of neural network, as it is based on a set of nodes or neurons that are connected to each other through topological relationships, while it is unsupervised because the observations are not previously labelled as pertaining to one or another category, but rather such a categorization is unknown. It was originally developed in the 1980s with the purpose of explaining the complex organization of the brains functions (see for an overview Kohonen and Honkela, 2007;Arribas-Bel et al, 2011); the primary objective was to reduce large and high-dimensional datasets, in terms of both the number of dimensions (projection) and the amount of initial observations (quantization), while maintaining the relevant information and presenting it in an understandable way for the human brain in order to uncover hidden patterns. Although its popularity has increased greatly over the years, and it has been used in many fields beyond those for which it was originally conceived, it has only been recently that the social sciences have become aware of its research potential.…”

Section: The Self-organizing Map Approachmentioning

confidence: 99%

High Performance in Complex Spatial Systems: A Self-Organizing Mapping Approach with Reference to the Netherlands

Kourtit

Arribas‐Bel²,

Nijkamp³

2013

SSRN Journal

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Section: The Self-organizing Map Approachmentioning

confidence: 99%

High Performance in Complex Spatial Systems: A Self-Organizing Mapping Approach with Reference to the Netherlands

Kourtit

Arribas‐Bel²,

Nijkamp³

2013

SSRN Journal

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“…Through multiple iterations, the weights of the neurons converge as the neighborhood of the best matching unit (BMU) shrinks (Ciampi and Lechevallier, 2000). The robustness of SOM clustering method could be associated with its characterized non-linear projection from the higher dimensional space of inputs to a low dimensional grid, which facilitates the discovery of hidden patterns in the input data (Kohonen and Honkela, 2007;Moghimidarzi et al, 2016). The SOM proved to be able to handle large datasets with outliers effectively (Shahreza et al, 2011;Oyana et al, 2012), and it has been applied successfully in complex structures (Tasdemir and Merényi, 2009).…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Enhanced velocity based pore pressure prediction using lithofacies clustering: A case study from a reservoir with complex lithology in Dezful Embayment, SW Iran

Arabameri¹,

soleymani²,

Tokhmechi³

2018

Preprint

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Velocity based pore pressure prediction methods are widely accepted as a routine technique in the petroleum industry. Despite recent improvements, still, literature suffer from inconsistencies and uncertainties mostly arise from velocity anomalies due to complex lithostratigraphic setting or presence of various formation fluids. The primary goal of this paper is to improve the accuracy and reliability of the conventional Bowers and Tau methods in a reservoir with complex lithology. Our proposed workflow aims to improve the accuracy of the estimations by clustering the input data based on specific petrophysical characteristics. We show since each major zones at the offset test wells have a distinct compaction trend, empirical constants in Bowers and Tau methods can be calibrated for each cluster rather than the whole stratigraphic column. The clustering task was done by statistical analyses of a suite of well logs and validated with core derived lithologies. To find the best clustering algorithm, we applied and compared five techniques namely, K-means, basic sequential algorithmic scheme, single, and complete linkage hierarchical. We found that the self-organizing map (SOM) method provides the best results by maximizing lithology likelihood within each cluster and improve the overall accuracy of the Bowers and Tau methods. This research also aims to provide a systematic comparison of the mentioned clustering algorithms based on their ability in distinguishing various lithofacies. We also try to minimize the user interference in the process of clustering multiple lithofacies and improve the reproducibility of the results and demonstrate the capability of the proposed method through a case study in a reservoir in the Southwest of Iran. Satisfactory results of this study offer a safe ground for implementation of the proposed method in other sedimentary basins.

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“…Another example of clustering algorithm is based on unsupervised learning using vector quantization namely linear vector quantization[LVQ] [16], SelfOrganization map[SOM] [17], and adaptive resonance theory models [18]. They are single layered architectures.…”

Section: F Artificial Neural Network For Clusteringmentioning

confidence: 99%