Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Image Classification using a Module RBF Neural Network
ICICIC '06 Proceedings of the First International Conference on Innovative Computing, Information and Control - Volume 2
On the principal domain of the discrete bispectrum of a stationarysignal
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
An evolutionary clustering algorithm for gene expression microarray data analysis
IEEE Transactions on Evolutionary Computation
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
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Automatic communication signal (e.g., FM signal) classification and identification focus on finding the fine feature contained in the almost approximate noisy communication signal comprehensively identifying the the same or different version of transmitters in modern electronic warfare. Direct use of HOS becomes unavailable for on-line application because of its huge computation time and memory space especially in the case of high frequency FM signal. This paper presents a novel view to improve the HOS analysis efficiency by sub-sampling while preserving the noise-contaminated fine feature and eliminating the random Gaussian noise. FM signal-related feature bispectra are also introduced to translate the 2-D feature matching pattern to a 1-D one applicable for an optimal adaptive k-means iterative RBF classifier. Computer simulations show that this novel feature bispectra outperform AIB and SB in terms of computation time and recognition rate for on-line steady FM signal recognition.