A Set-Medoids Vector Batch SOM Algorithm Based on Multiple Dissimilarity Matrices

2015 ◽  
Author(s):  
Francisco de A.T. de Carvalho ◽  
Eduardo C. Simoes
Keyword(s):  
Som Algorithm

scholarly journals Mental state space visualization for interactive modeling of personalized BCI control strategies

2019 ◽  
Author(s):  
Ilya Kuzovkin ◽  
Konstantin Tretyakov ◽  
Andero Uusberg ◽  
Raul Vicente
Keyword(s):  
State Space ◽  
Mental State ◽  
Control Strategies ◽  
Muscular Activity ◽  
Mental States ◽  
Learning System ◽  
Self Organizing Map ◽  
Preliminary Validation ◽  
Som Algorithm ◽  
Mental Actions

AbstractObjectiveNumerous studies in the area of BCI are focused on the search for a better experimental paradigm – a set of mental actions that a user can evoke consistently and a machine can discriminate reliably. Examples of such mental activities are motor imagery, mental computations, etc. We propose a technique that instead allows the user to try different mental actions in the search for the ones that will work best.ApproachThe system is based on a modification of the self-organizing map (SOM) algorithm and enables interactive communication between the user and the learning system through a visualization of user’s mental state space. During the interaction with the system the user converges on the paradigm that is most efficient and intuitive for that particular user.Main resultsResults of the two experiments, one allowing muscular activity, another permitting mental activity only, demonstrate soundness of the proposed method and offer preliminary validation of the performance improvement over the traditional closed-loop feedback approach.SignificanceThe proposed method allows a user to visually explore their mental state space in real time, opening new opportunities for scientific inquiry. The application of this method to the area of brain-computer interfaces enables more efficient search for the mental states that will allow a user to reliably control a BCI system.

scholarly journals Application of self organizing map in construction, geology and petroleum industry

2017 ◽  
Vol 20 (K4) ◽  
pp. 30-38
Author(s):  
Tung Son Pham ◽  
Huy Minh Truong ◽  
Tuan Ba Pham
Keyword(s):  
Industrial Revolution ◽  
Human Life ◽  
Petroleum Industry ◽  
Self Organizing Map ◽  
Practical Applications ◽  
Fourth Industrial Revolution ◽  
Som Algorithm ◽  
Clustering Data ◽  
Self Organizing

In recent years, Artificial Intelligence (AI) has become an emerging subject and been recognized as the flagship of the Fourth Industrial Revolution. AI is subtly growing and becoming vital in our daily life. Particularly, Self-Organizing Map (SOM), one of the major branches of AI, is a useful tool for clustering data and has been applied successfully and widespread in various aspects of human life such as psychology, economic, medical and technical fields like mechanical, construction and geology. In this paper, the primary purpose of the authors is to introduce SOM algorithm and its practical applications in geology and construction. The results are classification of rock facies versus depth in geology and clustering two sets of construction prices indices and building material costs indice.

scholarly journals An Advanced SOM Algorithm Applied to Handover Management Within LTE

2013 ◽  
Vol 62 (5) ◽  
pp. 1883-1894 ◽  
Author(s):  
Neil Sinclair ◽  
David Harle ◽  
Ian A. Glover ◽  
James Irvine ◽  
Robert C. Atkinson
Keyword(s):  
Handover Management ◽  
Som Algorithm

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

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6009
Author(s):  
Ignacio Sánchez Andrades ◽  
Juan J. Castillo Aguilar ◽  
Juan M. Velasco García ◽  
Juan A. Cabrera Carrillo ◽  
Miguel Sánchez Lozano
Keyword(s):  
Low Cost ◽  
Road Surface ◽  
Engineering Industry ◽  
Machine Learning Techniques ◽  
Self Organizing Map ◽  
Driver Assistance Systems ◽  
Self Organizing Maps ◽  
Learning Techniques ◽  
Som Algorithm ◽  
Self Organizing

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.

Weight-Value Convergence of the SOM Algorithm for Discrete Input

1998 ◽  
Vol 10 (4) ◽  
pp. 807-814 ◽  
Author(s):  
Siming Lin ◽  
Jennie Si
Keyword(s):  
Stationary State ◽  
Convergence Result ◽  
The Self ◽  
Self Organizing Map ◽  
Input Output ◽  
Weight Value ◽  
Som Algorithm ◽  
Self Organizing

Some insights on the convergence of the weight values of the self-organizing map (SOM) to a stationary state in the case of discrete input are provided. The convergence result is obtained by applying the Robbins-Monro algorithm and is applicable to input-output maps of any dimension.

Research on some problems in the Kohonen SOM algorithm

2003 ◽  
Author(s):  
Ying He ◽  
Tian-Jin Feng ◽  
Jun-Kuo Cao ◽  
Xiang-Qian Ding ◽  
Ying-Hui Zhou
Keyword(s):  
Som Algorithm ◽  
Kohonen Som
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