Modeling Time-Varying Dynamical Systems
提出了一种全局方法，用于识别、估计和预测具有确定性时变参数的传递函数模型，涉及稳定性分析、递归算法和伪线性回归技术。
Abstract A global methodology of identification, estimation and forecasting of transfer function (Box-Jenkins) models with deterministically varying parameters is provided. First, properties of stability and forecasting algorithms are investigated by means of Markovian representations and methods of solution of nonstationary difference equations. Next, the degree of the polynomials of the system is specified with typical off-line methods, and the shape of the coefficients (parameter functions) is identified by means of recursive (on-line) algorithms. Finally, the identified parameter functions are inserted in the model and their coefficients are estimated (off-line) on the original data by means of pseudolinear regression techniques.