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Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

By: , , , , and 
https://doi.org/10.1109/ICNC.2007.399

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Abstract

To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

Suggested Citation

Xue, Y., Liu, S., Hu, Y., Yang, J., Chen, Q., 2007, Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux, in Proceedings - Third International Conference on Natural Computation, ICNC 2007, Haikou, China, Aug 24-27, 2007, p. 183-187, https://doi.org/10.1109/ICNC.2007.399.

Publication type Conference Paper
Publication Subtype Conference Paper
Title Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux
DOI 10.1109/ICNC.2007.399
Year Published 2007
Language English
Publisher IEEE
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 5 p.
Larger Work Type Book
Larger Work Subtype Conference publication
Larger Work Title Proceedings - Third International Conference on Natural Computation, ICNC 2007
First page 183
Last page 187
Conference Title 3rd International Conference on Natural Computation, ICNC 2007
Conference Location Haikou, China
Conference Date Aug 24-27, 2007
Additional publication details