Robust Optimization Framework for Training Shallow Neural Networks Using Reachability Method

Yang, Yejiang; Xiang, Weiming

doi:10.1109/cdc45484.2021.9683106

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…To present the optimal performance of the proposed method, the noise in the interference parameter estimation results was ignored in the following numerical experiments. In practice, the influence of noise on prediction results could be mitigated by training in the presence of noise or introducing advanced training technology that could enhance the robustness of the prediction model [33,34].…”

Section: Results and Analysesmentioning

confidence: 99%

Online Sequential Extreme Learning Machine-Based Active Interference Activity Prediction for Cognitive Radar

et al. 2022

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For anti-active-interference-oriented cognitive radar systems, the mismatch between the acquired and actual interference information may result in serious degradation of cognitive anti-active-interference performance. To yield more effective knowledge of the electromagnetic environment and eliminate the mismatch effect, the electromagnetic activity prediction technique, which deduces future electromagnetic behaviors based on current observations, has received increasing attention. However, high computational complexities limit the application of conventional electromagnetic activity prediction methods in dynamic active interference prediction with high real-time requirements. In this paper, the online sequential extreme learning machine (OS-ELM)-based method, which is dedicated to high-efficiency active interference activity prediction, is proposed. The advancement includes two aspects. First, benefiting from the single-hidden-layer network structure and recursive-formula-based output weight updating, the proposed OS-ELM-based frequency prediction (OS-ELM-FP) and OS-ELM-based angle prediction (OS-ELM-AP) models can predict the interference state and update the prediction model parameters with much higher computational efficiency. Second, the better generalization performance enables the proposed method to achieve smaller interference activity prediction errors compared with conventional methods. Numerical examples and prediction results based on measured jamming data demonstrate the advantages of the proposed method.

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Section: Results and Analysesmentioning

confidence: 99%