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首頁> 外文期刊>Expert Systems with Application >Hybrid intelligent technique for automatic communication signals recognition using Bees Algorithm and MLP neural networks based on the efficient features
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Hybrid intelligent technique for automatic communication signals recognition using Bees Algorithm and MLP neural networks based on the efficient features

機(jī)譯：基于有效特征的基于Bees算法和MLP神經(jīng)網(wǎng)絡(luò)的混合智能技術(shù)用于自動(dòng)通信信號(hào)識(shí)別

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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.
機(jī)譯：自動(dòng)通信信號(hào)識(shí)別對于許多新穎的計(jì)算機(jī)和通信技術(shù)都起著重要的作用。大多數(shù)提出的技術(shù)只能識(shí)別幾種數(shù)字信號(hào)和/或它們的低階。它們通常需要高水平的信噪比（SNR）。在本文中，我們進(jìn)行了雙重研究。首先，我們提出了一種有效的系統(tǒng)，該系統(tǒng)使用頻譜特征和高達(dá)八分之一的高階矩以及高達(dá)八分之一的高階累積量的組合作為有效特征。作為分類器，我們使用了多層感知器（MLP）神經(jīng)網(wǎng)絡(luò)。在這一階段，我們研究了MLP神經(jīng)網(wǎng)絡(luò)的不同學(xué)習(xí)算法，其中包括快速支持（QP）學(xué)習(xí)算法，擴(kuò)展delta-bar-delta（EDBD），超自適應(yīng)反向傳播（SuperSAB）和共軛梯度（CG）等。 ）是在通信信號(hào)識(shí)別領(lǐng)域首次提出的。實(shí)驗(yàn)結(jié)果表明，即使在非常低的SNR情況下，所提出的系統(tǒng)也能以很高的精度區(qū)分許多數(shù)字通信信號(hào)。但是很多功能都用于這種識(shí)別。然后在第二個(gè)方面，為了降低識(shí)別器的復(fù)雜性，我們提出了一種新穎的混合智能技術(shù)。在這項(xiàng)技術(shù)中，我們優(yōu)化了Bees算法（BA）的分類器設(shè)計(jì)，以選擇提供給分類器的最佳功能。仿真結(jié)果表明，該算法具有很高的識(shí)別精度，并具有BA選擇的7個(gè)特征。

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