Modular Neural Network Task Decomposition Via Entropic Clustering
ISDA '06 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications - Volume 01
Moment-Based Techniques for Image Retrieval
DEXA '08 Proceedings of the 2008 19th International Conference on Database and Expert Systems Application
Review article: Aircraft recognition in infrared image using wavelet moment invariants
Image and Vision Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Moments and Moment Invariants in Pattern Recognition
Moments and Moment Invariants in Pattern Recognition
A generalized higher order neural network for aircraft recognition in a video docking system
Neural Computing and Applications
Aircraft identification by moment invariants
IEEE Transactions on Computers
Extreme learning machine for predicting HLA-Peptide binding
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
IEEE Transactions on Neural Networks
Real-time learning capability of neural networks
IEEE Transactions on Neural Networks
Robust radar target classifier using artificial neural networks
IEEE Transactions on Neural Networks
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In this paper, a novel recognition scheme is proposed for identifying the aircrafts of different types based on multiple modular neural network classifiers. Three moment invariants including Hu moments, Zernike moments and Wavelet moments are extracted from the characteristics exhibited by aircrafts and used as the input variables of each modular neural network respectively. Each modular neural network consists of multiple single-hidden layer feedforward networks which are trained using the extreme learning machine and different clustering data subsets. A clustering and selection method is used to get the classification rate of each modular neural network and then based on their weighted sum the final classification output is obtained. The proposed recognition scheme is finally evaluated by recognizing six different types of aircraft models and the simulation results show the superiority of the proposed method compared with the single ELM classifier and other classification algorithms.