Identification of MIMO Hammerstein systems using cardinal spline functions

A new approach to identify multivariable Hammerstein systems is proposed in this paper. By using cardinal cubic spline functions to model the static nonlinearities, the proposed method is effective in modelling processes with hard and/or coupled nonlinearities. With an appropriate transformation, the nonlinear models are parameterized such that the nonlinear identification problem is converted into a linear one. The persistently exciting condition for the transformed input is derived to ensure the estimates are consistent with the true system. A simulation study is performed to demonstrate the effectiveness of the proposed method compared with the existing approaches based on polynomials. (C) 2006 Elsevier Ltd. All rights reserved.

Impact of pre-existing sulfate on retention of imported chloride and nitrate in variable charge soil profiles

In variable charge soils, anion retention and accumulation through adsorption at exchange sites is a competitive process. The objectives of this study in the wet tropics of far north Queensland were to investigate (i) whether the pre-existing high sulphate in variable charge soils had any impact on the retention of chloride and nitrate, derived mostly from the applied fertilizer; and (ii) whether chloride competed with nitrate during the adsorption processes. Soil cores up to 12.5 m depth were taken from seven sites, representing four soil types, in the Johnstone River Catchment. Six of these sites had been under sugarcane (Saccharum officinarum-S) cultivation for at least 50 years and one was an undisturbed rainforest. The cores were segmented at 1.0 m depth increments, and subsamples were analysed for nitrate-N, cation (CEC)- and anion-exchange capacities (AEC), pH, exchangeable cations (Ca, Mg, K, Na), soil organic C (SOC), electrical conductivity (EC), sulphate-S, and chloride. Sulphate-S load in 1-12 m depth under cropping ranged from 9.4 to 73.9 t ha(-1) (mean= 40 t ha(-1)) compared with 74.4 t ha(-1) in the rainforest. Chloride load under cropping ranged from 1.5 to 9.6 t ha(-1) (mean= 4.9 t ha(-1)) compared to 0.9 t ha(-1) in the rainforest, and the nitrate-N load from 113 to 2760 kg ha(-1) (mean = 910 kg ha(-1)) under cropping compared to 12 kg ha(-1) in the rainforest. Regardless of the soil type, the total chloride or nitrate-N input in fertilisers was 7.5 t ha(-1), during the last 50 years. Sulphate-S distribution in soil profiles decreased with depth at >2 m, whereas bulges of chloride or nitrate-N were observed at depths >2 m. This suggests that chloride or nitrate adsorption and retention increased with decreasing sulphate dominance. Abrupt decreases in equivalent fraction of sulphate (EFSO4), at depths >2 m, were accompanied by rapid increases in equivalent fraction of chloride (EFCl), followed by nitrate (EFNO3). The stepwise regression for EFCl and EFNO3 indicated that nitrate retention was reduced by the pre-existing sulphate and imported chloride, whereas only sulphate reduced chloride adsorption. The results indicate that chloride and nitrate adsorption and retention occurred, in the order chloride>nitrate, in soils containing large amounts of sulphate under approximately similar total inputs of N- and Cl-fertilisers. (C) 2004 Elsevier B.V. All rights reserved.

A review of two journals found that articles using multivariable logistic regression frequently did not report commonly recommended assumptions

Background and Objective: To examine if commonly recommended assumptions for multivariable logistic regression are addressed in two major epidemiological journals. Methods: Ninety-nine articles from the Journal of Clinical Epidemiology and the American Journal of Epidemiology were surveyed for 10 criteria: six dealing with computation and four with reporting multivariable logistic regression results. Results: Three of the 10 criteria were addressed in 50% or more of the articles. Statistical significance testing or confidence intervals were reported in all articles. Methods for selecting independent variables were described in 82%, and specific procedures used to generate the models were discussed in 65%. Fewer than 50% of the articles indicated if interactions were tested or met the recommended events per independent variable ratio of 10: 1. Fewer than 20% of the articles described conformity to a linear gradient, examined collinearity, reported information on validation procedures, goodness-of-fit, discrimination statistics, or provided complete information on variable coding. There was no significant difference (P >.05) in the proportion of articles meeting the criteria across the two journals. Conclusion: Articles reviewed frequently did not report commonly recommended assumptions for using multivariable logistic regression. (C) 2004 Elsevier Inc. All rights reserved.

Forecasting Helicoverpa populations in Australia: A comparison of regression based models and a bio-climatic based modeling approach

Long-term forecasts of pest pressure are central to the effective management of many agricultural insect pests. In the eastern cropping regions of Australia, serious infestations of Helicoverpa punctigera (Wallengren) and H. armigera (Hübner)(Lepidoptera: Noctuidae) are experienced annually. Regression analyses of a long series of light-trap catches of adult moths were used to describe the seasonal dynamics of both species. The size of the spring generation in eastern cropping zones could be related to rainfall in putative source areas in inland Australia. Subsequent generations could be related to the abundance of various crops in agricultural areas, rainfall and the magnitude of the spring population peak. As rainfall figured prominently as a predictor variable, and can itself be predicted using the Southern Oscillation Index (SOI), trap catches were also related to this variable. The geographic distribution of each species was modelled in relation to climate and CLIMEX was used to predict temporal variation in abundance at given putative source sites in inland Australia using historical meteorological data. These predictions were then correlated with subsequent pest abundance data in a major cropping region. The regression-based and bioclimatic-based approaches to predicting pest abundance are compared and their utility in predicting and interpreting pest dynamics are discussed.