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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Target Recognition Methods Based on Multi-neural Network Classifiers Fusion
verfasst von : Huiying Dong, Shengfu Chen
Erschienen in: Advances in Neural Networks – ISNN 2013
Verlag: Springer Berlin Heidelberg
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In the paper, three kinds of classifiers are fused they are BP network classifier, self-organizing feature map network classifier and RBF network classifier and the moment invariant features as well as roundness features as the inputs of the fused neural network. Given targets are recognized by the majority voting method and self-adapts weighted fusion algorithm of the fused classifier, and also by the three network classifiers respectively. The recognition results of single neural network and fusion algorithm are analyzed and compared. The results indicate that the recognition rate of multi-neural networks fusion algorithm is higher than any single neural network, and also show that the fusion algorithm has the significance for improving the accuracy of recognition.