Classification of ancient Roman glazed ceramics using the neural network of Self-Organizing Maps

2000 ◽  
Vol 367 (6) ◽  
pp. 586-589 ◽  
Author(s):  
A. Lopez-Molinero ◽  
A. Castro ◽  
J. Pino ◽  
J. Perez-Arantegui ◽  
J. R. Castillo
Keyword(s):  
Neural Network ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
Self Organizing

FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK

2008 ◽  
Vol 18 (03) ◽  
pp. 233-256 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

Line Segment-Based Clustering Approach With Self-Organizing Maps

2021 ◽  
Vol 14 (4) ◽  
pp. 33-44
Author(s):  
G. Chamundeswari ◽  
G. P. S. Varma ◽  
C. Satyanarayana
Keyword(s):  
Neural Network ◽  
Line Segment ◽  
Feature Vector ◽  
Input Image ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
Clustering Techniques ◽  
Hough Transformation ◽  
The Neural Network ◽  
Self Organizing

Clustering techniques are used widely in computer vision and pattern recognition. The clustering techniques are found to be efficient with the feature vector of the input image. So, the present paper uses an approach for evaluating the feature vector by using Hough transformation. With the Hough transformation, the present paper mapped the points to line segment. The line features are considered as the feature vector and are given to the neural network for performing clustering. The present paper uses self-organizing map (SOM) neural network for performing the clustering process. The proposed method is evaluated with various leaf images, and the evaluated performance measures show the efficiency of the proposed method.

scholarly journals ERRATUM: "FLEXIBLE STRUCTURE MULTIPLE MODELING USING IRREGULAR SELF-ORGANIZING MAPS NEURAL NETWORK"

2008 ◽  
Vol 18 (04) ◽  
pp. 347-370 ◽  
Author(s):  
ALIREZA FATEHI ◽  
KENICHI ABE
Keyword(s):  
Neural Network ◽  
Minimum Spanning Tree ◽  
Model Space ◽  
Self Organizing Map ◽  
Local Models ◽  
Tree Graph ◽  
Self Organizing Maps ◽  
The Neural Network ◽  
The Neural Networks ◽  
Self Organizing

The MMSOM identification method, which had been presented by the authors, is improved to the multiple modeling by the irregular self-organizing map (MMISOM) using the irregular SOM (ISOM). Inputs to the neural networks are parameters of the instantaneous model computed adaptively at every instant. The neural network learns these models. The reference vectors of its output nodes are estimation of the parameters of the local models. At every instant, the model with closest output to the plant output is selected as the model of the plant. ISOM used in this paper is a graph of all the nodes and some of the weighted links between them to make a minimum spanning tree graph. It is shown in this paper that it is possible to add new models if the number of models is initially less than the appropriate one. The MMISOM shows more flexibility to cover the linear model space of the plant when the space is concave.

Land usage classification: a hierarchical neural network approach

2013 ◽  
Vol 152 (5) ◽  
pp. 817-828 ◽  
Author(s):  
E. J. PALOMO ◽  
D. ELIZONDO ◽  
G. BRUNSCHWIG
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Hierarchical Architecture ◽  
Forage Production ◽  
Self Organizing Maps ◽  
Massif Central ◽  
Land Usage ◽  
Hierarchical Neural Network ◽  
Self Organizing

SUMMARYThe classification of land usage in mountain grassland bovine areas is important for the management of forage production and grazing in grass-based livestock systems. The present paper proposes a novel, hierarchical neural network-based approach towards the classification of land usage in these areas. A survey of 72 farms was conducted in the Massif Central (France). Information was gathered on geographical characteristics and cutting and/or grazing practices on three general groups of fields: cut only, cut and grazed and grazed only fields. To classify land usage, the data were clustered and visualized in a hierarchical fashion. This was done by using a novel method for the analysis and classification of data based on growing hierarchical self-organizing maps (GHSOM). Self-organizing maps (SOM) have been shown to be successful for the analysis of highly dimensional input data in data mining applications as well as for data visualization. Moreover, the hierarchical architecture of the GHSOM is more flexible than a single SOM in the adaptation process to input data, capturing inherent hierarchical relationships among them. Experimental results show the utility of this approach.

Self-Organizing Neural Network Application to Drill Wear Classification

1994 ◽  
Vol 116 (2) ◽  
pp. 233-238 ◽  
Author(s):  
E. Govekar ◽  
I. Grabec
Keyword(s):  
Neural Network ◽  
Power Spectra ◽  
Processing System ◽  
Drilling Process ◽  
Feed Force ◽  
The Neural Network ◽  
On Line ◽  
Characteristic Features ◽  
Self Organizing

The article describes an application of a simulated neural network to drill wear classification from cutting force signals generated by the drilling process. As the input to the neural network, a multicomponent vector composed of a sensory part and a descriptive part is used. The components of the sensory part represent characteristic features of the cutting momentum and the feed force power spectra, while the descriptive part encodes the corresponding drill wear class. During adaptation, the self-organizing neural network is used to form a set of prototype vectors representing an empirical model of the observed drilling process. The model is used in the analysis mode of the system for an on-line classification of the drill wear from the cutting forces. The performance of the developed information processing system is experimentally demonstrated by classification of drill wear during machining on a steel workpiece.

Classification of Alloys with an Artificial Neural Network and Multivariate Calibration of Glow-Discharge Emission Spectra

1991 ◽  
Vol 45 (10) ◽  
pp. 1706-1716 ◽  
Author(s):  
Mark Glick ◽  
Gary M. Hieftje
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Glow Discharge ◽  
Multivariate Calibration ◽  
Emission Spectra ◽  
K Nearest Neighbor ◽  
The Neural Network ◽  
Calibration Sets ◽  
Artificial Neural

Artificial neural networks were constructed for the classification of metal alloys based on their elemental constituents. Glow discharge-atomic emission spectra obtained with a photodiode array spectrometer were used in multivariate calibrations for 7 elements in 37 Ni-based alloys (different types) and 15 Fe-based alloys. Subsets of the two major classes formed calibration sets for stepwise multiple linear regression. The remaining samples were used to validate the calibration models. Reference data from the calibration sets were then pooled into a single set to train neural networks with different architectures and different training parameters. After the neural networks learned to discriminate correctly among alloy classes in the training set, their ability to classify samples in the testing set was measured. In general, the neural network approach performed slightly better than the K-nearest neighbor method, but it suffered from a hidden classification mechanism and nonunique solutions. The neural network methodology is discussed and compared with conventional sample-classification techniques, and multivariate calibration of glow discharge spectra is compared with conventional univariate calibration.

scholarly journals Classification of Ascension Island and Natal ozonesondes using self-organizing maps

2012 ◽  
Vol 117 (D4) ◽  
pp. n/a-n/a ◽  
Author(s):  
Anders A. Jensen ◽  
Anne M. Thompson ◽  
F. J. Schmidlin
Keyword(s):  
Self Organizing Maps ◽  
Ascension Island ◽  
Self Organizing
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