Autolanding Control Using Recurrent Wavelet Elman Neural Network

Lin, Chih‐Min; Boldbaatar, Enkh-Amgalan

doi:10.1109/tsmc.2015.2389752

Cited by 32 publications

(10 citation statements)

References 28 publications

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“…Recently, with the development of deep learning, scholars proposed some neural network-based models to explore the semantic relations of Q&A texts [35], [36], which achieved great success on measuring answer quality [3], [4], [37]. Despite the deep belief networks (DBNs) [38] and recursive neural networks [39] have shown some nonlinear fitting capability, the great success of convolutional neural networks (CNNs) [40]- [42] and recurrent neural networks (RNNs) [43]- [45] on various tasks completely changed the research direction. For example, Severyn and Moschitti [3] employed a CNN to generate a representation for each sentence and then used similarity matrix to compute a relevant score.…”

Section: B Neural Network-based Approachesmentioning

confidence: 99%

Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

Jin

Chen

Zhao

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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In the area of community question answering (CQA), answer selection and answer ranking are two tasks which are applied to help users quickly access valuable answers. Existing solutions mainly exploit the syntactic or semantic correlation between a question and its related answers (Q&A), where the multifacet domain effects in CQA are still underexplored. In this paper, we propose a unified model, enhanced attentive recurrent neural network (EARNN), for both answer selection and answer ranking tasks by taking full advantages of both Q&A semantics and multifacet domain effects (i.e., topic effects and timeliness). Specifically, we develop a serialized long short-term memory to learn the unified representations of Q&A, where two attention mechanisms at either sentence level or word level are designed for capturing the deep effects of topics. Meanwhile, the emphasis of Q&A can be automatically distinguished. Furthermore, we design a time-sensitive ranking function to model the timeliness in CQA. To effectively train EARNN, a question-dependent pairwise learning strategy is also developed. Finally, we conduct extensive experiments on a realworld dataset from Quora. Experimental results validate the effectiveness and interpretability of our proposed EARNN model.

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Section: B Neural Network-based Approachesmentioning

confidence: 99%

Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

Jin

Chen

Zhao

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

View full text Add to dashboard Cite

show abstract

“…The connection results in that the context units always maintain a copy of the previous values of hidden units. Thus, the network can keep the past state, which is useful for applications such as sequence prediction [44][45][46]. In Figure 6, there are 46 neurons in the hidden layer.…”

Section: Ennmentioning

confidence: 99%

Neural Networks for Radar Waveform Recognition

et al. 2017

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For passive radar detection system, radar waveform recognition is an important research area. In this paper, we explore an automatic radar waveform recognition system to detect, track and locate the low probability of intercept (LPI) radars. The system can classify (but not identify) 12 kinds of signals, including binary phase shift keying (BPSK) (barker codes modulated), linear frequency modulation (LFM), Costas codes, Frank code, P1-P4 codesand T1-T4 codeswith a low signal-to-noise ratio (SNR). It is one of the most extensive classification systems in the open articles. A hybrid classifier is proposed, which includes two relatively independent subsidiary networks, convolutional neural network (CNN) and Elman neural network (ENN). We determine the parameters of the architecture to make networks more effectively. Specifically, we focus on how the networks are designed, what the best set of features for classification is and what the best classified strategy is. Especially, we propose several key features for the classifier based on Choi-Williams time-frequency distribution (CWD). Finally, the recognition system is simulated by experimental data. The experiments show the overall successful recognition ratio of 94.5% at an SNR of −2 dB.

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“…The fixed back connections result in context units always maintaining a copy of previous values of hidden units (since they propagate over the connections before the BP learning rule is applied). Thus, the network can maintain a sort of state, allowing it to perform such tasks as sequence-prediction that are beyond the power of a standard multi-layer perceptron [18,19]. In Figure 3, the hidden layer contains 46 neurons, sigmoid activation

f (x) = 1 / (1 + e^{- x})

is selected in each layer, and the number of input and output layers’ neurons are determined by the vector dimension, respectively.…”

Section: Waveform Classifiermentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Time-Frequency Distribution

Zhang

Liu

Diao

2016

Sensors

107

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In this paper, an automatic radar waveform recognition system in a high noise environment is proposed. Signal waveform recognition techniques are widely applied in the field of cognitive radio, spectrum management and radar applications, etc. We devise a system to classify the modulating signals widely used in low probability of intercept (LPI) radar detection systems. The radar signals are divided into eight types of classifications, including linear frequency modulation (LFM), BPSK (Barker code modulation), Costas codes and polyphase codes (comprising Frank, P1, P2, P3 and P4). The classifier is Elman neural network (ENN), and it is a supervised classification based on features extracted from the system. Through the techniques of image filtering, image opening operation, skeleton extraction, principal component analysis (PCA), image binarization algorithm and Pseudo–Zernike moments, etc., the features are extracted from the Choi–Williams time-frequency distribution (CWD) image of the received data. In order to reduce the redundant features and simplify calculation, the features selection algorithm based on mutual information between classes and features vectors are applied. The superiority of the proposed classification system is demonstrated by the simulations and analysis. Simulation results show that the overall ratio of successful recognition (RSR) is 94.7% at signal-to-noise ratio (SNR) of −2 dB.

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Autolanding Control Using Recurrent Wavelet Elman Neural Network

Cited by 32 publications

References 28 publications

Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

Promotion of Answer Value Measurement With Domain Effects in Community Question Answering Systems

Neural Networks for Radar Waveform Recognition

LPI Radar Waveform Recognition Based on Time-Frequency Distribution

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