The research paper addresses the problem of estimating aerodynamic parameters using a Gauss-Newton-based optimisation method. The process of the optimisation method lies on the principle of minimising the residual error between the measured and simulated responses of the system. Usually, the simulated response is obtained by integrating the dynamic equations of the system, which is found to be susceptible to the initial values, and the integration method. With the advent of the feedforward neural network, the data-driven regression methods have been widely used for identification of the system. Among them, a variant of feedforward neural network, extreme learning machine, which has proven the performance in terms of computational cost, generalisation, and so forth, has been addressed to predict the responses in the present study. The real flight data of longitudinal and lateral-directional motion have been considered to estimate their respective aerodynamic parameters. Furthermore, the estimates have been validated with the values of the classical estimation methods, such as the equation-error and filter-error methods. The sample standard deviations of the estimates demonstrate the effectiveness of the proposed method. Lastly, the proof-of-match exercise has been conducted with the other set of flight data to validate the estimated parameters.

This paper defines the ‘Case Deleted’ Deviance - a new objective function for evaluating Generalised Linear Models, and applies this to a number of practical examples in the pricing of general insurance. The paper details practical approximations to enable the efficient calculation of the objective, and derives modifications to the standard Generalised Linear Modelling algorithm to allow the derivation of scaled parameters from this measure to reduce potential over fitting to historical data. These scaled parameters improve the predictiveness of the model when applied to previously unseen data points, the most likely being related to future business written. The potential for over fitting has increased due to number of factors now used, particularly in pricing personal lines business and the advent of price comparison sites which has increased the penalties of mis-estimation. New material in this paper has been included in a UK patent application No. 1020091.3.

Four temperature response models were compared describing the emergence rate of Solanum sisymbriifolium (L.) over a broad range of suboptimal temperatures and at different soil water potentials. In the laboratory, the effects were tested on germination rates at constant (9.1–21.8°C) and diurnally fluctuating temperatures at different soil water potentials. Linear, Q10, expolinear and quadratic models were fitted to the data on rate of emergence against temperature. For model validation, field emergence was monitored in 11 sowings conducted in 2001–2004. Emergence rate increased with temperature and was relatively high at soil water potentials in the range of − 0.21 MPa to − 2.6 × 10− 3 MPa, but was almost zero at − 0.96 MPa and − 1.8 × 10− 3 MPa. Diurnal temperature fluctuations did not have a differing influence on germination rates or final germination percentages compared with constant temperatures. The expolinear and the quadratic models were most accurate in explaining variation of laboratory data, especially at temperatures close to the minimum germination temperature of S. sisymbriifolium. These two models had root mean square errors for predicting field emergence rates (5.9 to 38.4 d) of 0.81 and 0.87 d, respectively, and were considered more appropriate to predict the time to 50% germination for crops grown in conditions near their ‘base temperature’ than the widely used linear temperature (thermal time) models. The Gompertz function was fitted to percentage germination versus the time-accumulated germination rate (using the expolinear function to describe the rate–temperature relation). This combined model adequately predicted the temporal pattern of emergence in the field.