Kohonen Self-organizing Map (kohonen + self-organizing_map)

Distribution by Scientific Domains


Selected Abstracts


Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysis

JOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 7 2001
Marco Henrique Terra
In this article we discuss artificial neural networks-based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so-called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back-propagation algorithm and the RBFN is trained with a Kohonen self-organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3-joint planar manipulator. 2001 John Wiley & Sons, Inc. [source]


WELL LOG CALIBRATION OF KOHONEN-CLASSIFIED SEISMIC ATTRIBUTES USING BAYESIAN LOGIC

JOURNAL OF PETROLEUM GEOLOGY, Issue 4 2001
M. T. Taner
We present a new method for calibrating a classified 3D seismic volume. The classification process employs a Kohonen self-organizing map, a type of unsupervised artificial neural network; the subsequent calibration is performed using one or more suites of well logs. Kohonen self-organizing maps and other unsupervised clustering methods generate classes of data based on the identification of various discriminating features. These methods seek an organization in a dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues in the real world. In order to relate them to the real world, we must develop a calibration method that not only defines the relationship between the clusters and real physical properties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole dataset can then be calibrated. The clustering step reduces the multi-dimensional data into logically smaller groups. Each original data point defined by multiple attributes is reduced to a one- or two-dimensional relational group. This establishes some logical clustering and reduces the complexity of the classification problem. Furthermore, calibration should be more successful since it will have to consider less variability in the data. In this paper, we present a simple calibration method that employs Bayesian logic to provide the relationship between cluster centres and the real world. The output will give the most probable calibration between each self-organized map node and wellbore-measured parameters such as lithology, porosity and fluid saturation. The second part of the output comprises the calibration probability. The method is described in detail, and a case study is briefly presented using data acquired in the Orange River Basin, South Africa. The method shows promise as an alternative to current techniques for integrating seismic and log data during reservoir characterization. [source]


Electricity peak load forecasting with self-organizing map and support vector regression

IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, Issue 3 2006
Shu Fan Non-member
Abstract This paper aims to study the short-term peak load forecasting (PLF) by using Kohonen self-organizing maps (SOM) and support vector regression (SVR). We first adopt a SOM network to cluster the input data set into several subsets in an unsupervised learning strategy. Then, several SVRs for the next day's peak load are used to fit the training data of each subset in the second stage. In the numerical experiments, data of electricity demand from the New York Independent System Operator (ISO) are used to verify the effectiveness of the prediction for the proposed method. The simulation results show that the proposed model can predict the next day's peak load with a considerably high accuracy compared with the ISO forecasts. 2006 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. [source]


WELL LOG CALIBRATION OF KOHONEN-CLASSIFIED SEISMIC ATTRIBUTES USING BAYESIAN LOGIC

JOURNAL OF PETROLEUM GEOLOGY, Issue 4 2001
M. T. Taner
We present a new method for calibrating a classified 3D seismic volume. The classification process employs a Kohonen self-organizing map, a type of unsupervised artificial neural network; the subsequent calibration is performed using one or more suites of well logs. Kohonen self-organizing maps and other unsupervised clustering methods generate classes of data based on the identification of various discriminating features. These methods seek an organization in a dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues in the real world. In order to relate them to the real world, we must develop a calibration method that not only defines the relationship between the clusters and real physical properties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole dataset can then be calibrated. The clustering step reduces the multi-dimensional data into logically smaller groups. Each original data point defined by multiple attributes is reduced to a one- or two-dimensional relational group. This establishes some logical clustering and reduces the complexity of the classification problem. Furthermore, calibration should be more successful since it will have to consider less variability in the data. In this paper, we present a simple calibration method that employs Bayesian logic to provide the relationship between cluster centres and the real world. The output will give the most probable calibration between each self-organized map node and wellbore-measured parameters such as lithology, porosity and fluid saturation. The second part of the output comprises the calibration probability. The method is described in detail, and a case study is briefly presented using data acquired in the Orange River Basin, South Africa. The method shows promise as an alternative to current techniques for integrating seismic and log data during reservoir characterization. [source]


Image Segmentation and Bruise Identification on Potatoes Using a Kohonen's Self-Organizing Map

JOURNAL OF FOOD SCIENCE, Issue 7 2005
Thierry Marique
ABSTRACT: Potato quality includes a low incidence of colored bruises resulting from bad storage or manipulation practices. We developed a procedure to process and segment potato images by using Kohonen's self-organizing map. Anomalous regions could be distinguished on 3 potato varieties. Bruises that were very dissimilar in appearance were correctly identified, and some particular defects such as green spots could be located as well. [source]


Psychographic clustering of blood donors in Egypt using Kohonen's self-organizing maps

INTERNATIONAL JOURNAL OF NONPROFIT & VOLUNTARY SECTOR MARKETING, Issue 2 2010
Mohamed M. Mostafa
Blood donation has historically been marketed as one of the purest examples of altruistic or pro-social behavior. The recruitment of blood donors is, however, a challenge as transfusion centers worldwide strive to attract and retain blood donors. This study uses self-organizing maps (SOM) to examine the effect of various psychographic and cognitive factors on blood donation in Egypt. SOM is a machine learning method that can be used to explore patterns in large and complex datasets for linear and nonlinear patterns. The results show that major variables affecting blood donation are related to altruistic values, perceived risks of blood donation, blood donation knowledge, attitudes toward blood donation, and intention to donate blood. The study also shows that SOM models are capable of improving clustering quality while extracting valuable information from multidimensional data. Copyright 2009 John Wiley & Sons, Ltd. [source]