Self-organizing maps with information theoretic learning

Chalasani, Rakesh; Prı́ncipe, José C.

doi:10.1016/j.neucom.2013.12.059

Cited by 19 publications

(9 citation statements)

References 33 publications

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“…The key of the SOM algorithm is to update the BMU and its neighborhood nodes concurrently. Input topology of dataset is preserved on the output nodes through performance of such a mapping [65]. Details of the implementation procedure of SOM are described in the Appendix A.…”

Section: Self-organizing Mapmentioning

confidence: 99%

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Ren

Chu

et al. 2020

Remote Sensing

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Numerical models and remote sensing observation systems such as radars are useful for providing information on surface flows for coastal areas. Evaluation of their performance and extracting synoptic characteristics are challenging and important tasks. This research aims to investigate synoptic characteristics of surface flow fields through undertaking a detailed analysis of model results and high frequency radar (HFR) data using self-organizing map (SOM) and empirical orthogonal function (EOF) analysis. A dataset of surface flow fields over thirteen days from these two sources was used. A SOM topology map of size 4 × 3 was developed to explore spatial patterns of surface flows. Additionally, comparisons of surface flow patterns between SOM and EOF analysis were carried out. Results illustrate that both SOM and EOF analysis methods are valuable tools for extracting characteristic surface current patterns. Comparisons indicated that the SOM technique displays synoptic characteristics of surface flow fields in a more detailed way than EOF analysis. Extracted synoptic surface current patterns are useful in a variety of applications, such as oil spill treatment and search and rescue. This research provides an approach to using powerful tools to diagnose ocean processes from different aspects. Moreover, it is of great significance to assess SOM as a potential forecasting tool for coastal surface currents.

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Section: Self-organizing Mapmentioning

confidence: 99%

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Ren

Chu

et al. 2020

Remote Sensing

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“…To implement the SOM author uses Matlab Neural network toolbox [29]. More about the SOM and its learning algorithm can be found in the literature [40,41].…”

Section: Self-organizing Mapmentioning

confidence: 99%

Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

Głowacz

2016

Teh. vjesn.

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Original scientific paper A system of fault diagnostics of loaded synchronous motor was proposed. Proposed system was based on acoustic signals of loaded synchronous motor. A new method of feature extraction SMOFS-25-EXPANDED (shorted method of frequencies selection-25-Expanded) was proposed. Presented method was analysed for 3 classifiers: LDA (Linear Discriminant Analysis), NN (Nearest Neighbour), SOM (Self-organizing Map). Analysis was carried out for real incipient states of loaded synchronous motor. Acoustic signals generated by motor were used in analysis. The following states of motor were analysed: healthy motor, motor with shorted stator coil, motor with shorted stator coil and broken coil, motor with shorted stator coil and two broken coils. These states are caused by natural degradation of rotating synchronous motor. The results of recognition were good. Proposed method of acoustic signal recognition can be used to protect loaded synchronous motors. Keywords: acoustic signal; fault detection; loaded synchronous motor; recognitionDijagnostika greške akustičkih signala opterećenog sinkronog motora primjenom SMOFS-25-EXPANDED i odabranih klasifikatora Izvorni znanstveni članak Predlaže se sustav dijagnostike greške opterećenog sinkronog motora. Predloženi se sustav zasniva na akustičkim signalima opterećenog sinkronog motora. Predlaže se nova metoda određivanja karakteristika SMOFS-25-EXPANDED (shorted method of frequencies selection-25-expaned -skraćena metoda za izbor frekvencija-25-proširena). Predložena se metoda analizirala u odnosu na tri klasifikatora: LDA (Linear Discriminant Analysis -analiza linearnog diskriminatora), NN (Nearest Neighbour -najbliži susjed), SOM (Self-organizing Map -samoorganizirajuća mapa). Analiza je provedena za stvarno početna stanja opterećenog sinkronog motora. U analizi su iskorišteni akustički signali koje je stvarao motor. Analizirala su se sljedeća stanja motora: ispravan motor, motor sa skraćenim namotajem statora, motor sa skraćenim namotajem statora i prekinutim namotajem, motor sa skraćenim namotajem statora i dva prekinuta namotaja. Do takvih je stanja došlo zbog prirodne degradacije rotirajućeg sinkronog motora. Rezultati analize su dobri. Predložena metoda prepoznavanja akustičkog signala može se primijeniti za zaštitu opterećenih sinkronih motora.

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“…Thus, if the BA attempts to process a large number of samples with high-dimensional feature space, it is difficult to maintain feasible computational time, and the likelihood calculation is highly unstable, and global convergence is difficult to achieve. 14 KBA applies KBR 15 instead of the general Bayes' Theorem in BA, and a Correntropy 16 -based alternative similarity measurement called CIM 17 to solve the primitive issues of Bayesian computation which are described above. However, due to the original idea of ART, any input is considered as useful data for clustering.…”

Section: Introductionmentioning

confidence: 99%

A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

Masuyama

Loo

Wermter

2019

Int. J. Neur. Syst.

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This paper attempts to solve the typical problems of self-organizing growing network models, i.e. (a) an influence of the order of input data on the self-organizing ability, (b) an instability to high-dimensional data and an excessive sensitivity to noise, and (c) an expensive computational cost by integrating Kernel Bayes Rule (KBR) and Correntropy-Induced Metric (CIM) into Adaptive Resonance Theory (ART) framework. KBR performs a covariance-free Bayesian computation which is able to maintain a fast and stable computation. CIM is a generalized similarity measurement which can maintain a high-noise reduction ability even in a high-dimensional space. In addition, a Growing Neural Gas (GNG)-based topology construction process is integrated into the ART framework to enhance its self-organizing ability. The simulation experiments with synthetic and real-world datasets show that the proposed model has an outstanding stable self-organizing ability for various test environments.

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Self-organizing maps with information theoretic learning

Cited by 19 publications

References 33 publications

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Investigations into Synoptic Spatiotemporal Characteristics of Coastal Upper Ocean Circulation Using High Frequency Radar Data and Model Output

Fault diagnostics of acoustic signals of loaded synchronous motor using SMOFS-25-EXPANDED and selected classifiers

A Kernel Bayesian Adaptive Resonance Theory with A Topological Structure

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