Hybrid neural network techniques for storm system identification and tracking - Parikh, Jo Ann; Daponte, John S; Vitale, Joseph N (IEEE, 1999)
Accurate identification and tracking of synoptic-scale storm systems in the northern midlatitudes is important for understanding the structure and movement of the midlatitude cloud field which plays a major role in climate change. 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 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 linear discriminant analysis, k-nearest neighbor rule, and backpropagation neural network techniques.
Keywords: Backpropagation; General circulation models; Genetic algorithms; Hybrid neural networks; Meteorology; Neural networks; Optimization; Storms; Tracking (position); dept.csc; ⛔ No DOI found