Risk-Sensitive and/or Adaptive Identification of Dynamic Systems by Neural Networks

该项目将解决在串联-并联和并联公式中识别动态系统的鲁棒性和适应性的基本问题。这两个问题是近20年来系统识别、控制和滤波领域的主要研究热点。一般来说，系统标识符应该对值得自适应的环境参数具有自适应性，对不值得自适应的环境参数具有鲁棒性。采用受人工神经网络（ann）发展启发的综合方法，它将奠定数学基础，开发方法，并测试其可行性，用于项目中动态系统的鲁棒和/或自适应识别。自适应辨识的主要思想是将自适应神经辨识器的非线性权值和线性权值分别作为长记忆和短时记忆，前者在事先离线训练中确定，后者通过LMS或RLS算法在线调整。期望这种自适应神经辨识器在运行过程中具有计算量最小化、专注于学习和适应值得自适应的环境参数、消除性能面局部极值差等优点。这个想法的灵感来自于大脑进行适应的方式。

This project will address the fundamental issues of robustness and adaptiveness in the identification of dynamic systems in both series-parallel and parallel formulations. These two issues have been the topics of major concentrated research activities in system identification, control and filtering in the past 20 years. generally speaking, a system identifier should be adaptive to adaptation-worthy environmental parameters and robust to these which are adptative-unworthy. Taking a synthetic approach inspired by the development of the artificial neural networks (ANNs) it will lay the mathematical foundations, develop the methodologies, and test their feasibility's for robust and/or adaptive identification of dynamic systems in the project. The main idea for adaptive identification is to use the nonlinear and linear weights of an adaptive neural identifier as the long and short-term memory respectively, the former being determined in a prior off-line training and the latter adjusted on-line by an LMS or RLS algorithm. Such an adaptive neural identifier is expected to have the advantages of minimizing computation, focusing on learning about and adapting to the adaptation-worthy environmental parameters, and eliminating poor local extreme of the performance surface during the operation of the adaptive neural identifier. This idea was inspired by the way a brain performs adaptation.