Recent research carried out in CRES has produced a data-based modelling approach to the adaptive forecasting of river flow or levels using rainfall or upstream flow inputs as leading indicators. This approach, which is based on the estimation of a general transfer function model from historical data, has significant advantages over existing flood forecasting methods: for example, on-line adjust...

We show that a hierarchical Bayesian modeling approach allows us to perform regularization in sequential learning. We identify three inference levels within this hierarchy: model selection, parameter estimation, and noise estimation. In environments where data arrive sequentially, techniques such as cross validation to achieve regularization or model selection are not possible. The Bayesian app...

This work presents set-membership adaptive algorithms based on time-varying error bounds for CDMA interference suppression. We introduce a modified family of set-membership adaptive algorithms for parameter estimation with time-varying error bounds. The algorithms considered include modified versions of the set-membership normalized least mean squares (SM-NLMS), the affine projection (SM-AP) an...

Introduction: It has been demonstrated that Optimal Sampling Schedule (OSS) theory [1] can be used as an objective strategy for choosing a TI sampling scheme in pulsed arterial spin labelling (PASL) experiments, yielding an improvement in parameter estimation precision [2]. However, this requires a priori estimates of the likely value of the fitted parameters. Adaptive Sequential Design (ASD) a...

the forecasting of hydrological variables, such as streamflow, plays an important role in water resource planning and management. recently, the development of stochastic models is regarded as a major step for this purpose. streamflow forecasting using the arima model can be conducted when unknown parameters are estimated correctly because parameter estimation is one of the crucial steps in mode...

Based on virtual equivalent system concept and methodology, a unified analysis of the stability and convergence of switching multiple model adaptive control (MMAC) of linear time-invariant discrete plant is presented. The main results are expressed by three criteria. Two of them are applicable to switching MMAC systems with arbitrary control strategy, arbitrary parameter estimation algorithm an...