Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

An efficient Riemann solver for unsteady flows with non-ideal gases

A Riemann solver is presented for the Euler equations of gas dynamics with real gases. This represents a more efficient version of an algorithm originally presented by the author.

Fractional order and non-linear system identification algorithms for biomedical applications

We discuss the modelling of dielectric responses of amorphous biological samples. Such samples are commonly encountered in impedance spectroscopy studies as well as in UV, IR, optical and THz transient spectroscopy experiments and in pump-probe studies. In many occasions, the samples may display quenched absorption bands. A systems identification framework may be developed to provide parsimonious representations of such responses. To achieve this, it is appropriate to augment the standard models found in the identification literature to incorporate fractional order dynamics. Extensions of models using the forward shift operator, state space models as well as their non-linear Hammerstein-Wiener counterpart models are highlighted. We also discuss the need to extend the theory of electromagnetically excited networks which can account for fractional order behaviour in the non-linear regime by incorporating nonlinear elements to account for the observed non-linearities. The proposed approach leads to the development of a range of new chemometrics tools for biomedical data analysis and classification.

The tragedy of non-ideal theory

This paper has three aims. First, it argues that the present use of ‘ideal theory’ is unhelpful, and that an earlier and apparently more natural use focusing on perfection would be preferable. Second, it has tried to show that revision of the use of the term would better expose two distinctive normative issues, and illustrated that claim by showing how some contributors to debates about ideal theory have gone wrong partly through not distinguishing them. Third, in exposing those two distinct normative issues, it has revealed a particular way in which ideal theory even under the more specific, revisionary definition will often be central to working out what to do in non-ideal circumstances. By clarifying the terms on which debates over ideal and non-ideal theory should take place, and highlighting the particular problems each deals with, it tries to clear the ground for turning to the actual problem, which is what to do in our non-ideal and often tragic circumstances.

Design, implementation and characterisation of a contactless power transfer system for rotating applications

This paper describes the design, implementation and characterisation of a contactless power transfer system for rotating applications. The power transfer system is based upon a zero-voltage-switched, full-bridge, DC-DC converter, but utilises a non-standard transformer. This transformer allows power transfer between its primary and secondary windings while also allowing free rotation between these windings. The aim of this research is to develop a solution that could replace mechanical slip-rings in certain applications where a non-contacting system would be advantageous. Based upon the design method presented in this paper, a 2 kW prototype system is constructed. Results obtained from testing the 2 kW prototype are presented and discussed. This discussion considers how the performance of the transformer varies with rotation and also the overall efficiency of the system

A stable one-step-ahead predictive control of non-linear systems

In this paper stability of one-step ahead predictive controllers based on non-linear models is established. It is shown that, under conditions which can be fulfilled by most industrial plants, the closed-loop system is robustly stable in the presence of plant uncertainties and input–output constraints. There is no requirement that the plant should be open-loop stable and the analysis is valid for general forms of non-linear system representation including the case out when the problem is constraint-free. The effectiveness of controllers designed according to the algorithm analyzed in this paper is demonstrated on a recognized benchmark problem and on a simulation of a continuous-stirred tank reactor (CSTR). In both examples a radial basis function neural network is employed as the non-linear system model.

Local oscillator phase lock under multipath for video deghoster systems

Under multipath conditions, standard Video Intermediate Frequency (VIF) detectors generate a local oscillator phase error and consequently produce a dispersed non-ideal detected video signal due to the presence of additional IF carriers. The dispersed video causes problems when attempting to identify and remove the multipath interference, or ghosts, by the use of Digital Signal Processing and digital filtering. A digital phase lock system is presented which derives the correct phase for synchronous detection in the presence of multipath by using correlation information that has already been calculated as part of the deghosting process. As a result, the video deghoster system is made simpler, faster and more economical.

Conservative upwind difference schemes for the Euler equations for real gas flows in a duct

In a recent paper [P. Glaister, Conservative upwind difference schemes for compressible flows in a Duct, Comput. Math. Appl. 56 (2008) 1787–1796] numerical schemes based on a conservative linearisation are presented for the Euler equations governing compressible flows of an ideal gas in a duct of variable cross-section, and in [P. Glaister, Conservative upwind difference schemes for compressible flows of a real gas, Comput. Math. Appl. 48 (2004) 469–480] schemes based on this philosophy are presented for real gas flows with slab symmetry. In this paper we seek to extend these ideas to encompass compressible flows of real gases in a duct. This will incorporate the handling of additional terms arising out of the variable geometry and the non-ideal nature of the gas.

Linear-wavelet models applied to the identification of a two-link manipulator

This paper shows that a wavelet network and a linear term can be advantageously combined for the purpose of non linear system identification. The theoretical foundation of this approach is laid by proving that radial wavelets are orthogonal to linear functions. A constructive procedure for building such nonlinear regression structures, termed linear-wavelet models, is described. For illustration, sim ulation data are used to identify a model for a two-link robotic manipulator. The results show that the introduction of wavelets does improve the prediction ability of a linear model.

Adaptive modelling with tunable RBF network using multi-innovation RLS algorithm assisted by swarm intelligence

In this paper, we propose a new on-line learning algorithm for the non-linear system identification: the swarm intelligence aided multi-innovation recursive least squares (SI-MRLS) algorithm. The SI-MRLS algorithm applies the particle swarm optimization (PSO) to construct a flexible radial basis function (RBF) model so that both the model structure and output weights can be adapted. By replacing an insignificant RBF node with a new one based on the increment of error variance criterion at every iteration, the model remains at a limited size. The multi-innovation RLS algorithm is used to update the RBF output weights which are known to have better accuracy than the classic RLS. The proposed method can produces a parsimonious model with good performance. Simulation result are also shown to verify the SI-MRLS algorithm.

Playing Kant at the Court of King Arthur

This article contrasts the sense in which those whom Bernard Williams called ‘political realists’ and John Rawls are committed to the idea that political philosophy has to be distinctively political. Distinguishing the realist critique of political moralism from debates over ideal and non-ideal theory, it is argued that Rawls is more realist than many realists realise, and that realists can learn more about how to make a distinctively political vision of how our life together should be organised from his theorising, although it also points to a worrying tendency among Rawlsians to reach for inappropriately moralised arguments. G. A. Cohen’s advocacy of socialism and the second season of HBO’s The Wire are used as examples to illustrate these points.

Time-varying coefficients in the almost ideal demand system: An empirical appraisal

This paper provides a generalisation of the structural time series version of the Almost Ideal Demand System (AIDS) that allows for time-varying coefficients (TVC/AIDS) in the presence of cross-equation constraints. An empirical appraisal of the TVC/AIDS is made using a dynamic AIDS with trending intercept as the baseline model with a data set from the Italian Household Budget Survey (1986-2001). The assessment is based on four criteria: adherence to theoretical constraints, statistical diagnostics on residuals, forecasting performance and economic meaningfulness. No clear evidence is found for superior performance of the TVC/AIDS, apart from improved short-term forecasts.