Grid topologies for the self-organizing map

López‐Rubio, Ezequiel; Ramos, Antonio Díaz

doi:10.1016/j.neunet.2014.05.001

Cited by 11 publications

(4 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The connections between adjacent neurons define the SOM topology. The SOM can preserve the topology in the projection of the input data from highdimensional space onto the two-dimensional SOM lattice in a way that relative distances between data points are preserved 57,58 Different SOM topologies have been investigated by several researchers 57,59,60 . The neurons are commonly connected via square or hexagonal topology.…”

Section: Self-organizing Map Clustering (Som)mentioning

confidence: 99%

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

Lethy

Othman

Gabry

et al. 2021

Preprint

View full text Add to dashboard Cite

A combination of multiple discrimination artificial neural networks using different seismic source parameters is suggested using a committee machine. In this work, a committee machine was used to combine supervised and unsupervised artificial neural networks to discriminate between earthquakes and quarry blasts using data from the Egyptian National Seismological Network (ENSN). The unsupervised network is used as a measure of accuracy for the results of the supervised neural network. The unsupervised Self-Organized Map (SOM) and the k-means clustering algorithms are used to estimate support and confidence measures for the results. Meanwhile, the supervised neural network is used to discriminate between earthquakes and explosions. The artificial neural networks are trained using different input parameters which are the P wave spectrum corner frequency (PcF), S wave corner frequency (ScF), and the ratio (Rcf) of PcF to Scf. The combined approach succeeds to discriminate between earthquakes and quarry blasts in Northern Egypt. The method provides the results with a measure of confidence which eliminates false discrimination. The current paper represents an idea to implement artificial intelligence to assist experts in decision-making situations. The committee machine could identify the nature of a particular event, using the aid of several discrimination methods. The proposed committee machine could combine the results of several algorithms and expert opinions to form one single output with a confidence measure.

show abstract

Section: Self-organizing Map Clustering (Som)mentioning

confidence: 99%

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

Lethy

Othman

Gabry

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In order to obtain a topology preservation measure which treats all possible map topologies on equal terms, the Mean Tied Rank (M T R) can be used [15]. For each test sample we compute the list of all the units of the map which are not the first best matching unit, sorted by topological distance to the first best matching unit.…”

Section: Unsupervised Clusteringmentioning

confidence: 99%

The Role of the Lattice Dimensionality in the Self-Organizing Map

Ramos¹,

López‐Rubio²,

Palomo³

2018

NNW

Self Cite

View full text Add to dashboard Cite

The Self-Organizing Map model considers the possibility of 1D and 3D map topologies. However, 2D maps are by far the most used in practice. Moreover, there is a lack of a theory which studies the relative merits of 1D, 2D and 3D maps. In this paper a theory of this kind is developed, which can be used to assess which topologies are better suited for vector quantization. In addition to this, a broad set of experiments is presented which includes unsupervised clustering with machine learning datasets and color image segmentation. Statistical significance tests show that the 1D maps perform significantly better in many cases, which agrees with the theoretical study. This opens the way for other applications of the less popular variants of the self-organizing map.

show abstract

“…This allows for the information to be visually analyzed, which improves its analysis and interpretation. The SOM algorithm is capable of generating clusters of data that have similarities generating topological relationships on a predefined grid using the Unsupervised Learning paradigm [ 4 , 5 ]. The topology of the generated network makes it possible to discern whether or not a surface belongs to each of the generated clusters.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Andrades

Aguilar

García

et al. 2020

Sensors

View full text Add to dashboard Cite

Expanding the performance and autonomous-decision capability of driver-assistance systems is critical in today’s automotive engineering industry to help drivers and reduce accident incidence. It is essential to provide vehicles with the necessary perception systems, but without creating a prohibitively expensive product. In this area, the continuous and precise estimation of a road surface on which a vehicle moves is vital for many systems. This paper proposes a low-cost approach to solve this issue. The developed algorithm resorts to analysis of vibrations generated by the tyre-rolling movement to classify road surfaces, which allows for optimizing vehicular-safety-system performance. The signal is analyzed by means of machine-learning techniques, and the classification and estimation of the surface are carried out with the use of a self-organizing-map (SOM) algorithm. Real recordings of the vibration produced by tyre rolling on six different types of surface were used to generate the model. The efficiency of the proposed model (88.54%) and its speed of execution were compared with those of other classifiers in order to evaluate its performance.

show abstract

Grid topologies for the self-organizing map

Cited by 11 publications

References 65 publications

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

Discrimination between Small Earthquakes and Local Quarry Blasts Using Committee Machine

The Role of the Lattice Dimensionality in the Self-Organizing Map

Low-Cost Road-Surface Classification System Based on Self-Organizing Maps

Contact Info

Product

Resources

About