A modular ridge randomized neural network with differential evolutionary distributor applied to the estimation of sea ice thickness

A modular ridge randomized neural network with differential evolutionary distributor applied to... In this paper, a sequential intelligent methodology is implemented to estimate the sea-ice thickness along the Labrador coast of Canada based on spatio-temporal information from the moderate resolution imaging spectro-radiometer, and the advanced microwave scanning radiometer-earth sensors. The proposed intelligent model comprises two separate sub-systems. In the first part of the model, clustering is performed to divide the studied region into a set of sub-regions, based on a number of features. Thereafter, this learning system serves as a distributor to dispatch the proper information to a set of estimation modules. The estimation modules utilize ridge randomized neural network to create a map between a set of features and sea-ice thickness. The proposed modular intelligent system is best suited for the considered case study as the amount of collected spatio-temporal information is large. To ascertain the veracity of the proposed technique, two different spatio-temporal databases are considered, which include the remotely sensed brightness temperature data at two different frequencies (low frequency, 6.9 GHz, and high frequency, 36.5 GHz) in addition to both atmospheric and oceanic variables coming from validated forecasting models. To numerically prove the accuracy and computational robustness of the designed sequential learning system, two different sets of comparative tests are conducted. In the first phase, the emphasis is put on evaluating the efficacy of the proposed modular framework using different clustering methods and using different types of estimators at the heart of the estimation modules. Thereafter, the modular estimator is prepared with standard neural identifiers to prove to what extent the modular estimator can increase the accuracy and robustness of the estimation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

A modular ridge randomized neural network with differential evolutionary distributor applied to the estimation of sea ice thickness

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2016 by Springer-Verlag Berlin Heidelberg
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-016-2074-5
Publisher site
See Article on Publisher Site

Abstract

In this paper, a sequential intelligent methodology is implemented to estimate the sea-ice thickness along the Labrador coast of Canada based on spatio-temporal information from the moderate resolution imaging spectro-radiometer, and the advanced microwave scanning radiometer-earth sensors. The proposed intelligent model comprises two separate sub-systems. In the first part of the model, clustering is performed to divide the studied region into a set of sub-regions, based on a number of features. Thereafter, this learning system serves as a distributor to dispatch the proper information to a set of estimation modules. The estimation modules utilize ridge randomized neural network to create a map between a set of features and sea-ice thickness. The proposed modular intelligent system is best suited for the considered case study as the amount of collected spatio-temporal information is large. To ascertain the veracity of the proposed technique, two different spatio-temporal databases are considered, which include the remotely sensed brightness temperature data at two different frequencies (low frequency, 6.9 GHz, and high frequency, 36.5 GHz) in addition to both atmospheric and oceanic variables coming from validated forecasting models. To numerically prove the accuracy and computational robustness of the designed sequential learning system, two different sets of comparative tests are conducted. In the first phase, the emphasis is put on evaluating the efficacy of the proposed modular framework using different clustering methods and using different types of estimators at the heart of the estimation modules. Thereafter, the modular estimator is prepared with standard neural identifiers to prove to what extent the modular estimator can increase the accuracy and robustness of the estimation.

Journal

Soft ComputingSpringer Journals

Published: Feb 18, 2016

References

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