Robust and/or Adaptive Neural Networks for Dynamic System Identification

用于动态系统识别的鲁棒和/或自适应神经网络

• 批准号: 0114619
• 负责人: James Lo
• 金额: $ 23.29万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-07-01 至 2006-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0114619&HistoricalAwards=false
• 关键词: Robust; Adaptive; Neural; Networks; Dynamic

0114619LoIn a preceding project supported by NSF, mathematical justification and numerical feasibility of neural networks (NNs) with long- and short-term memories (LASTMs) and risk-sensitive NNs have been established for adaptive and robust identification of dynamic systems respectively. Equally important, an adaptive method of training NNs that has the ability to select a training criterion most suitable for the training data and to avoid poor local minima of the selected training criterion has been discovered. The objective of the new project is to further develop these ideas and methodologies, conduct thorough benchmarking studies, and develop more powerful algorithms in order to complete establishing this neurocomputing approach to robust and/or adaptive identification of dynamic systems.Among the key tasks are: (1) development of an online recursive algorithm for adjusting linear weights of an NN with LASTMs in the presence of multicollinearity, using possibly a combination of the Kalman filter and ridge regression; (2) comparison of adaptive risk-seeking training method against existing methods using robust estimation criteria from statistics; (3) development of an algorithm using risk-seeking and risk-averting criteria alternately in identifying a dynamic system with a fine feature or an under-represented segment in the presence of outlying measurement noises; (4) development of a theory of the convergence properties of the adaptive risk-averting training method conceived in the preceding project; (5) combination of the above adaptive and robust system identification ideas for identification of a dynamic system in an uncertain environment.

0114619在国家自然科学基金资助的前一个项目中，已经建立了具有长期和短期记忆(LASTM)的神经网络(NN)和风险敏感神经网络(NN)的数学证明和数值可行性，以分别用于动态系统的自适应辨识和稳健辨识。同样重要的是，已经发现了一种训练NN的自适应方法，该方法具有选择最适合于训练数据的训练标准并避免所选训练标准的局部极小值的能力。新项目的目标是进一步发展这些思想和方法，进行深入的基准研究，并开发更强大的算法，以完成建立这种神经计算方法来稳健和/或自适应识别动态系统。其中关键任务包括：(1)开发一种在线递归算法，用于在存在多重共线性的情况下调整具有LASTM的神经网络的线性权重，可能使用卡尔曼滤波和岭回归的组合；(2)比较自适应风险寻求训练方法与现有方法使用统计稳健估计准则；(3)开发了在存在异常测量噪声的情况下交替使用风险寻求和风险规避准则来识别具有良好特征的动态系统或表示不足的分段的算法；(4)发展了先前项目中设想的自适应风险规避训练方法的收敛性质的理论；(5)将上述自适应和鲁棒系统辨识思想相结合，用于在不确定环境中辨识动态系统。