摘要翻译：
非线性时变系统自适应控制的复杂性要求采用具有较低计算复杂度的新方法，并在参数变化时保证良好的性能。在本研究中，我们利用小波基自适应匹配追踪演算法，对参数时变的非线性系统进行在线辨识与控制。我们将所提出的在线辨识与控制方案应用于两个不同的非线性系统辨识与控制基准实例。仿真结果表明，该算法采用小波基自适应匹配追踪算法，即使存在时变参数，也能有效地辨识和控制非线性系统。
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英文标题：
《Adaptive Matching Pursuit based Online Identification and Control Scheme
  for Nonlinear Systems》
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作者：
Hamid Khodabandehlou
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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英文摘要：
  The complexity of adaptive control of nonlinear time-varying systems requires the use of novel methods that have lower computational complexity as well as ensuring good performance under time-varying parameter changes. In this study, we use adaptive matching pursuit algorithm with wavelet bases for an online identification and control of the nonlinear system with time-varying parameters. We apply the proposed online identification and control scheme to two different benchmark examples of nonlinear system identification and control. Simulation results show that the proposed algorithm, using adaptive matching pursuit with wavelet bases, can effectively identify and control the nonlinear system even in presence of time-varying parameters.
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PDF链接：
https://arxiv.org/pdf/1803.01897