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• W2085776338 abstract "Research highlights? We propose a novel system that can identify many kinds of digital signal types from low density to high density. ? We propose a system that works very well in low level of SNRs. ? For the first time in domain of digital communication signal recognition we have used the bees algorithm for feature selection and applied the extended delta-bar-delta (EDBD) algorithm and conjugate gradient (CG) algorithm. ? We propose a system that needs a lower sample of signals and little number of features for recognition of a lot of digital communication signals. Automatic communication signal recognition plays an important role for many novel computer and communication technologies. Most of the proposed techniques can only identify a few kinds of digital signal and/or low order of them. They usually require high levels of signal to noise ratio (SNR). In this paper, we investigate twofold. First, we propose an efficient system that uses a combination set of spectral characteristics and higher order moments up to eighth and higher order cumulants up to eighth as the effective features. As the classifier we used a multi-layer perceptron (MLP) neural network. In this stage we investigate different learning algorithms of MLP neural networks that some of them, such as quick prop (QP) learning algorithm, extended delta-bar-delta (EDBD), super self adaptive back propagation (SuperSAB) and conjugate gradient (CG) are proposed for the first time in the area of communication signals recognition. Experimental results show that proposed system discriminates a lot of digital communication signals with high accuracy even at very low SNRs. But a lot of features are used for this recognition. Then at the second fold, in order to reduce the complexity of the recognizer, we have proposed a novel hybrid intelligent technique. In this technique we have optimized the classifier design by Bees Algorithm (BA) for selection of the best features that are fed to the classifier. Simulation results show that the proposed technique has very high recognition accuracy with seven features selected by BA." @default.
• W2085776338 created "2016-06-24" @default.
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• W2085776338 date "2011-05-01" @default.
• W2085776338 modified "2023-09-25" @default.
• W2085776338 title "Hybrid intelligent technique for automatic communication signals recognition using Bees Algorithm and MLP neural networks based on the efficient features" @default.
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• W2085776338 doi "https://doi.org/10.1016/j.eswa.2010.11.021" @default.
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