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Research on Chaotic Signal Reconstruction Algorithm Based on Artificial Intelligence

机译:基于人工智能的混沌信号重构算法研究

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In order to improve the ability of reconstruction modeling and analysis of chaotic signal in wireless network, it is necessary to optimize modeling and phase space reconstruction of chaotic signal in wireless network. An artificial intelligence-based algorithm for chaotic signal reconstruction of wireless network is proposed, constructs the influence model of phase space reconstruction on chaotic signal of wireless network, uses fractional Fourier transform for dynamic compression of chaotic signal of wireless network, extracts the spectral characteristic of chaotic signal of wireless network, uses adaptive beamforming method for beam-focus analysis and spectral feature analysis of chaotic signal of wireless network in phase space reconstruction, and uses blind source separation method to realize optimal detection and recognition of chaotic signal of wireless network in phase space reconstruction. The simulation results show that this method can improve the feature extraction and detection ability of chaotic signal in the phase space reconstruction, and has strong anti-interference ability for signal detection and modeling.
机译:为了提高无线网络中混沌信号的重构建模和分析能力,有必要对无线网络中混沌信号的建模和相空间重构进行优化。提出了一种基于人工智能的无线网络混沌信号重构算法,构造了相空间重构对无线网络混沌信号的影响模型,利用分数傅里叶变换对无线网络混沌信号进行动态压缩,提取了频谱特征。无线网络混沌信号,在相空间重构中采用自适应波束形成方法进行无线网络混沌信号的波束聚焦分析和频谱特征分析,并采用盲源分离法实现无线网络混沌信号的最优检测与识别。空间重建。仿真结果表明,该方法可以提高相空间重构中混沌信号的特征提取和检测能力,对信号的检测和建模具有很强的抗干扰能力。

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