DocumentCode :
1804354
Title :
Hybrid neural network techniques for storm system identification and tracking
Author :
Parikh, Jo Ann ; DaPonte, J.S. ; Vitale, Joseph N.
Author_Institution :
Dept. of Comput. Sci., Southern Connecticut State Univ., New Haven, CT, USA
Volume :
6
fYear :
1999
fDate :
36342
Firstpage :
4125
Abstract :
In this paper a hybrid neural network/genetic algorithm (NN/GA) approach is presented that analyzes the behavior of storm systems from one time frame to the next. The goal of the hybrid neural network algorithm is to improve the classifier output by reducing the number of infeasible solutions using constraint optimization techniques. The input to the hybrid neural network algorithm is the output from a traditional backpropagation neural network. The hybrid NN/GA analyzes the backpropagation neural network output for logical consistencies and makes changes to the classification results based on strength of neural network classifications and satisfaction of logical constraints. The results are compared with classification results obtained using the linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques
Keywords :
backpropagation; genetic algorithms; geophysics computing; neural nets; pattern classification; storms; tracking; backpropagation; constraint optimization; genetic algorithm; neural network; pattern classification; storm; tracking; Backpropagation algorithms; Clouds; Computer science; Constraint optimization; Neural networks; Predictive models; Remote sensing; Storms; Tracking; Tropical cyclones;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
ISSN :
1098-7576
Print_ISBN :
0-7803-5529-6
Type :
conf
DOI :
10.1109/IJCNN.1999.830824
Filename :
830824
Link To Document :
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