1,625 research outputs found
Asymptotic Level Density of the Elastic Net Self-Organizing Feature Map
Whileas the Kohonen Self Organizing Map shows an asymptotic level density
following a power law with a magnification exponent 2/3, it would be desired to
have an exponent 1 in order to provide optimal mapping in the sense of
information theory. In this paper, we study analytically and numerically the
magnification behaviour of the Elastic Net algorithm as a model for
self-organizing feature maps. In contrast to the Kohonen map the Elastic Net
shows no power law, but for onedimensional maps nevertheless the density
follows an universal magnification law, i.e. depends on the local stimulus
density only and is independent on position and decouples from the stimulus
density at other positions.Comment: 8 pages, 10 figures. Link to publisher under
http://link.springer.de/link/service/series/0558/bibs/2415/24150939.ht
An Application of Clustering Analysis to International Private Indebtedness
This paper presents a procedure for clustering analysis that combines Kohoneâs Self organizing Feature Map (SOFM) and statistical schemes. The idea is to cluster the data in two stages: run SOFM and then minimize the segmentation dispersion. The advantages of proposed procedure will be illustrated through a synthetic experiment and a real macroeconomic problem. The procedure is then used to explore the relationship between private indebtedness and some macroeconomic variables commonly used to measure macroeconomic performance. The experiences of thirty-nine countries in the early nineties are analyzed. The procedure outperformed others clustering techniques in the job of identifying consistent groups of countries from the economic and statistical viewpoints. It found out similarities in different countries concerning their respective levels of private indebtedness when added to well accepted parameters to measure macroeconomic performance.Vector quantization, Clustering, Self-Organizing Feature Map,Macroeconomic Performance, Private Indebtedness.
An efficient initialization scheme for the self-organizing feature map algorithm
[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters of the algorithm, namely, the learning-rate parameter and the neighborhood function. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Two data sets are tested to illustrate the performance of the proposed method.[[conferencetype]]åé[[conferencedate]]19990710~19990716[[booktype]]įīæŽ[[conferencelocation]]Washington, DC, US
Modeling of Social Transitions Using Intelligent Systems
In this study, we reproduce two new hybrid intelligent systems, involve three
prominent intelligent computing and approximate reasoning methods: Self
Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory
(RST),called SONFIS and SORST. We show how our algorithms can be construed as a
linkage of government-society interactions, where government catches various
states of behaviors: solid (absolute) or flexible. So, transition of society,
by changing of connectivity parameters (noise) from order to disorder is
inferred
Designing fuzzy rule based classifier using self-organizing feature map for analysis of multispectral satellite images
We propose a novel scheme for designing fuzzy rule based classifier. An SOFM
based method is used for generating a set of prototypes which is used to
generate a set of fuzzy rules. Each rule represents a region in the feature
space that we call the context of the rule. The rules are tuned with respect to
their context. We justified that the reasoning scheme may be different in
different context leading to context sensitive inferencing. To realize context
sensitive inferencing we used a softmin operator with a tunable parameter. The
proposed scheme is tested on several multispectral satellite image data sets
and the performance is found to be much better than the results reported in the
literature.Comment: 23 pages, 7 figure
Improving the self-organizing feature map algorithm using an efficient initialization scheme
[[abstract]]It is often reported in the technique literature that the success of the self-organizing feature map formation is critically dependent on the initial weights and the selection of main parameters (i.e. the learning-rate parameter and the neighborhood set) of the algorithm. They usually have to be counteracted by the trial-and-error method; therefore, often time consuming retraining procedures have to precede before a neighborhood preserving feature amp is obtained. In this paper, we propose an efficient initialization scheme to construct an initial map. We then use the self-organizing feature map algorithm to make small subsequent adjustments so as to improve the accuracy of the initial map. Several data sets are tested to illustrate the performance of the proposed method.[[notice]]čĢæĢåŪ
Communications and control for electric power systems: Power flow classification for static security assessment
This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed
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