Spatial interpolation of in situ data by self-organizing map algorithms (neural networks) for the assessment of carbon stocks in European forests

2010 ◽  
Vol 260 (3) ◽  
pp. 287-293 ◽  
Author(s):  
Wolfgang Stümer ◽  
Bernhard Kenter ◽  
Michael Köhl
Keyword(s):  
Neural Networks ◽  
Spatial Interpolation ◽  
Carbon Stocks ◽  
Self Organizing Map ◽  
European Forests ◽  
In Situ Data ◽  
Self Organizing

Assessment of Self-Organizing Map artificial neural networks for the classification of sediment quality

2008 ◽  
Vol 34 (6) ◽  
pp. 782-790 ◽  
Author(s):  
Manuel Alvarez-Guerra ◽  
Cristina González-Piñuela ◽  
Ana Andrés ◽  
Berta Galán ◽  
Javier R. Viguri
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sediment Quality ◽  
Self Organizing Map ◽  
Artificial Neural ◽  
Self Organizing

scholarly journals Variations in the summer oceanic <i>p</i>CO<sub>2</sub> and carbon sink in Prydz Bay using the self-organizing map analysis approach

2019 ◽  
Vol 16 (3) ◽  
pp. 797-810 ◽  
Author(s):  
Suqing Xu ◽  
Keyhong Park ◽  
Yanmin Wang ◽  
Liqi Chen ◽  
Di Qi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Open Ocean ◽  
Sea Surface ◽  
Prydz Bay ◽  
Self Organizing Map ◽  
Ocean Region ◽  
Co2 Sink ◽  
Shelf Region ◽  
Self Organizing

Abstract. This study applies a neural network technique to produce maps of oceanic surface pCO2 in Prydz Bay in the Southern Ocean on a weekly 0.1∘ longitude × 0.1∘ latitude grid based on in situ measurements obtained during the 31st CHINARE cruise from February to early March 2015. This study area was divided into three regions, namely, the “open-ocean” region, “sea-ice” region and “shelf” region. The distribution of oceanic pCO2 was mainly affected by physical processes in the open-ocean region, where mixing and upwelling were the main controls. In the sea-ice region, oceanic pCO2 changed sharply due to the strong change in seasonal ice. In the shelf region, biological factors were the main control. The weekly oceanic pCO2 was estimated using a self-organizing map (SOM) with four proxy parameters (sea surface temperature, chlorophyll a concentration, mixed Layer Depth and sea surface salinity) to overcome the complex relationship between the biogeochemical and physical conditions in the Prydz Bay region. The reconstructed oceanic pCO2 data coincide well with the in situ pCO2 data from SOCAT, with a root mean square error of 22.14 µatm. Prydz Bay was mainly a strong CO2 sink in February 2015, with a monthly averaged uptake of 23.57±6.36 TgC. The oceanic CO2 sink is pronounced in the shelf region due to its low oceanic pCO2 values and peak biological production.

Performance Analysis Using a Deep Belief Network by a Self-Organizing Map

2021 ◽  
pp. 2150007
Author(s):  
Yongquan Yan ◽  
Yu Zhu ◽  
Yanjun Li
Keyword(s):  
Neural Networks ◽  
Linear Trend ◽  
Time Series Prediction ◽  
Resource Consumption ◽  
Series Data ◽  
Self Organizing Map ◽  
Software Aging ◽  
Existing Problems ◽  
Consumption Prediction ◽  
Self Organizing

Since resource consumption is the main reason for software aging occurrences, many methods have been applied to accurately predict the resource consumption series. Among these methods, neural networks are powerfully applied to forecast the series data. For some existing problems of artificial neural networks such as the choice of initialization and local optimization, the improvements of neural networks are not only a hot research topic in the field of time series prediction but also a research hotspot in resource consumption prediction of software aging. In this paper, we propose a method for resource consumption prediction of software aging using deep belief nets (DBNs) with the restricted Boltzmann machine (RBM). This presented method contains the following steps. First, a pre-processing is introduced by two parts: smoothing data by a self-organizing map (SOM) and removing a linear trend by a difference method. Second, a method, DBN with two RBMs, is presented to capture the features and forecast future values. Third, a glowworm swarm optimization (GSO) method is used to learn the hyper-parameters of DBN with two RBMs. In the experiments, two types of resource consumption series are used to validate our proposed method compared with some state-of-the-art algorithms.

scholarly journals SOMO-mOptimization Algorithm with Multiple Winners

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Wei Wu ◽  
Atlas Khan
Keyword(s):  
Neural Networks ◽  
Objective Function ◽  
Learning Process ◽  
Numerical Experiments ◽  
Iteration Process ◽  
Competitive Learning ◽  
Self Organizing Map ◽  
Self Organizing ◽  
Information Sciences

Self-organizing map (SOM) neural networks have been widely applied in information sciences. In particular, Su and Zhao proposes in (2009) an SOM-based optimization (SOMO) algorithm in order to find a wining neuron, through a competitive learning process, that stands for the minimum of an objective function. In this paper, we generalize the SOM-based optimization (SOMO) algorithm to so-called SOMO-malgorithm withmwinning neurons. Numerical experiments show that, form>1, SOMO-malgorithm converges faster than SOM-based optimization (SOMO) algorithm when used for finding the minimum of functions. More importantly, SOMO-malgorithm withm≥2can be used to find two or more minimums simultaneously in a single learning iteration process, while the original SOM-based optimization (SOMO) algorithm has to fulfil the same task much less efficiently by restarting the learning iteration process twice or more times.

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