scholarly journals Using Self-Organizing Maps for Rural Territorial Typology

2011 ◽  
pp. 107-126 ◽  
Author(s):  
Marcos Santos da Silva ◽  
Edmar Ramos de Siqueira ◽  
Olívio Teixeira ◽  
Maria Manos ◽  
Antônio Monteiro
Keyword(s):  
Neural Network ◽  
Temporal Analysis ◽  
The Self ◽  
Self Organizing Map ◽  
Clustering Methods ◽  
Self Organizing Maps ◽  
Policy Makers ◽  
Spatial Temporal Analysis ◽  
Self Organizing

This work assessed the capacity of the self-organizing map, an unsupervised artificial neural network, to aid the process of territorial design through visualization and clustering methods applied to a multivariate geospatial temporal dataset. The method was applied in the case study of Sergipe‘s institutional regional partition (Territories of Identity). Results have shown that the proposed method can improve the exploratory spatial-temporal analysis capacity of policy makers that are interested in territorial typology. A new partition for rural planning was elaborated and confirmed the coherence of the Territories of Identity.

Credit Rating Classification Using Self-Organizing Maps

2002 ◽  
pp. 140-153 ◽  
Author(s):  
Roger P.G.H. Tan ◽  
Jan van den Berg ◽  
Willem-Max van den Bergh
Keyword(s):  
Credit Rating ◽  
Point Of View ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Self Organizing

In this case study, we apply the Self-Organizing Map (SOM) technique to a financial business problem. The case study is mainly written from an investor’s point of view giving much attention to the insights provided by the unique visualization capabilities of the SOM. The results are compared to results from other, more common, econometric techniques. Because of limitations of space, our description is quite compact in several places. For those interested in more details, we refer to Tan (2000).

The self-organizing map in synoptic climatological research

2011 ◽  
Vol 35 (1) ◽  
pp. 109-119 ◽  
Author(s):  
Scott C. Sheridan ◽  
Cameron C. Lee
Keyword(s):  
Spatial Patterns ◽  
The Self ◽  
Synoptic Climatology ◽  
Self Organizing Map ◽  
Two Dimensional ◽  
Clustering Methods ◽  
Traditional Methods ◽  
Self Organizing Maps ◽  
Dimensional Array ◽  
Self Organizing

Self-organizing maps (SOMs) are a relative newcomer to synoptic climatology; the method itself has only been utilized in the field for around a decade. In this article, we review the major developments and climatological applications of SOMs in the literature. The SOM can be used in synoptic climatological analysis in a manner similar to most other clustering methods. However, as the results from a SOM are generally represented by a two-dimensional array of cluster types that ‘self-organize’, the synoptic categories in the array effectively represent a continuum of synoptic categorizations, compared with discrete realizations produced through most traditional methods. Thus, a larger number of patterns can be more readily understood, and patterns, as well as transitional nodes between patterns, can be discerned. Perhaps the most intriguing development with SOMs has been the new avenues of visualization; the resultant spatial patterns of any variable can be more readily understood when displayed in a SOM. This improved visualization has led to SOMs becoming an increasingly popular tool in various research with climatological applications from other disciplines as well.

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

CENTROID NEURAL NETWORK WITH WEIGHTED FEATURES

2009 ◽  
Vol 18 (08) ◽  
pp. 1353-1367 ◽  
Author(s):  
DONG-CHUL PARK
Keyword(s):  
Neural Network ◽  
Image Compression ◽  
Objective Function ◽  
Lagrange Multiplier ◽  
Synthetic Data ◽  
Feature Weighting ◽  
The Self ◽  
Self Organizing Map ◽  
Data Set ◽  
Self Organizing

A Centroid Neural Network with Weighted Features (CNN-WF) is proposed and presented in this paper. The proposed CNN-WF is based on a Centroid Neural Network (CNN), an effective clustering tool that has been successfully applied to various problems. In order to evaluate the importance of each feature in a set of data, a feature weighting concept is introduced to the Centroid Neural Network in the proposed algorithm. The weight update equations for CNN-WF are derived by applying the Lagrange multiplier procedure to the objective function constructed for CNN-WF in this paper. The use of weighted features makes it possible to assess the importance of each feature and to reject features that can be considered as noise in data. Experiments on a synthetic data set and a typical image compression problem show that the proposed CNN-WF can assess the importance of each feature and the proposed CNN-WF outperforms conventional algorithms including the Self-Organizing Map (SOM) and CNN in terms of clustering accuracy.

Modeling and Production of Robot Trajectories Using the Temporal Parametrized Self Organizing Maps

2003 ◽  
Vol 13 (02) ◽  
pp. 119-127 ◽  
Author(s):  
Antonio Carlos Padoan ◽  
Guilherme de A. Barreto ◽  
Aluizio F. R. Araújo
Keyword(s):  
Inverse Kinematics ◽  
The Self ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Neural Architecture ◽  
New States ◽  
Simulation Results ◽  
Self Organizing

In this paper we proposed an unsupervised neural architecture, called Temporal Parametrized Self Organizing Map (TEPSOM), capable of learning and reproducing complex robot trajectories and interpolating new states between the learned ones. The TEPSOM combines the Self-Organizing NARX (SONARX) network, responsible for coding the temporal associations of the robotic trajectory, with the Parametrized Self-Organizing (PSOM) network, responsible for an efficient interpolation mechanism acting on the SONARX neurons. The TEPSOM network is used to model the inverse kinematics of the PUMA 560 robot during the execution of trajectories with repeated states. Simulation results show that the TEPSOM is more accurate than the SONARX in the reproduction of the learned trajectories.

Using the Self-Organizing Map as a Visualization Tool in Financial Benchmarking

2003 ◽  
Vol 2 (3) ◽  
pp. 171-181 ◽  
Author(s):  
Tomas Eklund ◽  
Barbro Back ◽  
Hannu Vanharanta ◽  
Ari Visa
Keyword(s):  
Performance Analysis ◽  
Pulp And Paper ◽  
The Self ◽  
Financial Data ◽  
Visualization Tool ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Time Period ◽  
The World ◽  
Self Organizing

In this paper, we illustrate the use of the self-organizing map technique for financial performance analysis and benchmarking. We build a database of financial ratios indicating the performance of 91 international pulp and paper companies for the time period 1995–2001. We then use the self-organizing map technique to analyze and benchmark the performance of the five largest pulp and paper companies in the world. The results of the study indicate that by using the self-organizing maps, we are able to structure, analyze, and visualize large amounts of multidimensional financial data in a meaningful manner.

scholarly journals A HYBRID KANSEI ENGINEERING SYSTEM USING THE SELF-ORGANIZING MAP NEURAL NETWORK

2016 ◽  
Vol 2 (1) ◽  
pp. 23-38
Author(s):  
C.S. Teh ◽  
C.P. Lim
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Product Design ◽  
The Self ◽  
Design Parameters ◽  
Decision Making Process ◽  
Engineering System ◽  
Self Organizing Map ◽  
Kansei Engineering ◽  
Self Organizing

Kansei Engineering (KE), a technology founded in Japan initially for product design, translates human feelings into design parameters. Although various intelligent approaches to objectively model human functions and the relationships with the product design decisions have been introduced in KE systems, many of the approaches are not able to incorporate human subjective feelings and preferences into the decision-making process. This paper proposes a new hybrid KE system that attempts to make the machine-based decision-making process closely resembles the real-world practice. The proposed approach assimilates human perceptive and associative abilities into the decision-making process of the computer. A number of techniques based on the Self-Organizing Map (SOM) neural network are employed in the backward KE system to reveal the underlying data structures that are involved in the decision-making process. A case study on interior design is presented to evaluate the efficacy of the proposed approach. The results obtained demonstrate the effectiveness of the proposed approach in developing an intelligent KE system which is able to combine human feelings and preferences into its decision making process.

scholarly journals Haptic Perception with Self-Organizing ANNs and an Anthropomorphic Robot Hand

2010 ◽  
Vol 2010 ◽  
pp. 1-9 ◽  
Author(s):  
Magnus Johnsson ◽  
Christian Balkenius
Keyword(s):  
Neural Network ◽  
Haptic Perception ◽  
Cell Structure ◽  
The Self ◽  
Robot Hand ◽  
Self Organizing Map ◽  
Training Set ◽  
Input Space ◽  
Growing Cell ◽  
Self Organizing

We have implemented and compared four biologically motivated self-organizing haptic systems based on proprioception. All systems employ a 12-d.o.f. anthropomorphic robot hand, the LUCS Haptic Hand 3. The four systems differ in the kind of self-organizing neural network used for clustering. For the mapping of the explored objects, one system uses a Self-Organizing Map (SOM), one uses a Growing Cell Structure (GCS), one uses a Growing Cell Structure with Deletion of Neurons (GCS-DN), and one uses a Growing Grid (GG). The systems were trained and tested with 10 different objects of different sizes from two different shape categories. The generalization abilities of the systems were tested with 6 new objects. The systems showed good performance with the objects from the training set as well as in the generalization experiments. Thus the systems could discriminate individual objects, and they clustered the activities into small cylinders, large cylinders, small blocks, and large blocks. Moreover, the self-organizing ANNs were also organized according to size. The GCS-DN system also evolved disconnected networks representing the different clusters in the input space (small cylinders, large cylinders, small blocks, large blocks), and the generalization samples activated neurons in a proper subnetwork in all but one case.

Task Allocation of Multiple Robotic Fishes Based on Self-Organizing Map Neural Network

2014 ◽  
Vol 563 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Lian Jiang
Keyword(s):  
Neural Network ◽  
Task Allocation ◽  
Robotic Fish ◽  
The Self ◽  
Self Organizing Map ◽  
Ball Game ◽  
Target Task ◽  
Water Polo ◽  
Som Neural Network ◽  
Self Organizing

For a water polo ball game there are multiple water polos and multiple robotic fishes in each team, seeking a reasonable task allocation plan is the key point to win the game. To resolve the problem, this paper proposed a multi-target task allocation method based on the Self-organizing map (SOM) neural network. This method takes the position of the water polos as the input vector, competes and compares the position of the water polos and robotic fishes, outputs the corresponding robotic fish of each water polo. The robotic fish will move toward the target water polo when the weight was adjusted, and will finally reach the target water polo. Simulations show that the score of the team using this method is higher than another team. The results prove the correctness and reliability of this method.

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