Robust adaptive identification of dynamic systems by neural networks
J.T. Lo · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
This paper is concerned with the use of neural networks for robust and adaptive identification of dynamic systems. Two types of neural identifiers, that are robust to adaptation-unworthy environmental parameters and adaptive to adaptation-worthy ones, are discussed, one requiring online weight adjustment and the other not. Risk-sensitive criteria are proposed for both training neural identifiers off-line and adjusting their weights online. These criteria induce robust performance by emphasising large errors in an exponential manner. A robust adaptive neural identifier without online weight adjustment is a time lagged recurrent network (TLRN) synthesized from input/output data of the dynamic system at with respect to a risk-sensitive criterion. The neural identifier's ability to adapt to the adaptation-worthy environmental parameters is a manifestation of the ability of the TLRN to estimate these parameters internally. A robust adaptive neural identifier with online weight adjustment is a neural network with long- and short-term memories. The long-term memory, which consists of the nonlinear weights of the neural network, is determined in an a priori off-line training in such a way that it is independent of the adaptation-worthy variables. The short-term memory, which consists of the linear weights of the neural network, is adjusted online to adapt to the adaptation-worthy variables. The criteria used for both the off-line determination of the long-term memory and the online adjustment of the short-term memory are risk-sensitive to induce the neural identifier's robust performance.