APPLICATION OF FAST ORTHOGONAL SEARCH TO LINEAR AND NONLINEAR STOCHASTIC-SYSTEMS

Standard deterministic autoregressive moving average (ARMA) models con sider prediction errors to be unexplainable noise sources. The accurac y of the estimated ARMA model parameters depends on producing minimum prediction errors. In this study, an accurate algorithm is developed f or estimating linear and nonlinear stochastic ARMA model parameters by using a method known as fast orthogonal search, with an extended mode l containing prediction errors as part of the model estimation process . The extended algorithm uses fast orthogonal search in a two-step pro cedure in which deterministic terms in the nonlinear difference equati on model are first identified and then reestimated, this time in a mod el containing the prediction errors. Since the extended algorithm uses an orthogonal procedure, together with automatic model order selectio n criteria, the significant model terms are estimated efficiently and accurately. The model order selection criteria developed for the exten ded algorithm are also crucial in obtaining accurate parameter estimat es. Several simulated examples are presented to demonstrate the effica cy of the algorithm.