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The data-based mechanistic (DBM) approach to modelling has developed as a stochastic, ‘top-down’ response to the problems associated with the deterministic, ‘bottom-up’ approach. As such, it can be compared with the deterministic, top-down modelling methods that have been attracting attention recently in the hydrological literature. Using catchment-scale rainfall–flow modelling as an example, this paper compares the inductive DBM approach with its hypothetico-deductive, deterministic alternative and shows how they can be used to identify and estimate low-order, nonlinear models of the rainfall–flow dynamics in the River Hodder catchment of northwest England based on a limited set of rainfall–flow data. Copyright © 2003 John Wiley & Sons, Ltd.

An Unobserved Components (UC) Model based on an enhanced version of the Dynamic Harmonic Regression model, including new multi-rate and modulated cycle procedures, is used to develop a customised package for forecasting and signal extraction applied to hourly telephone call numbers made to Barclaycard plc. service centres, with a forecasting horizon of up to several weeks in advance. The paper outlines both the methodological and algorithmic aspects of the modelling, forecasting and signal extraction procedures, including the design and implementation of forecasting support software with a specially designed Graphical User Interface within the Matlab® computing environment. The forecasting performance is evaluated comprehensively in comparison with the well-known seasonal ARIMA approach.

Risk management is formally defined as the process by which an organization assesses and addresses its risks. Historically, the role of risk management has been associated with insurance-buying, occupational safety and health, and legal liability management. In recent years managers and physicians alike have begun to recognize that organizational risks are pervasive, that these risks are extraordinarily diverse and complex, and that these risks are not just confined to "insurable" or accident-related situations. They may include risks arising from actions of regulatory bodies, third party payers, hospitals, partners, and employees, in addition to the physiatrist's personal or business investment, management and clinical practice. Furthermore, changing customer and patient preferences and/or expectations make the assessment of risk an even more dynamic and continuous process. This article describes the formal risk management process and suggests ways that physiatrists can apply risk management to their business and clinical practice. In developing this description, physiatrists and their office managers will learn about the overall goals and objectives of risk management, the challenge of identifying and analyzing risks, the tools and treatment options available, and the means by which risk management efforts are effectively implemented.

Macropodid herpesvirus 1 (MaHV-1) is an unclassified alphaherpesvirus linked with the fatal infections of kangaroos and other marsupials. During the characterisation of the internal repeat region of MaHV-1, an open reading frame (ORF) encoding for thymidylate synthase (TS) gene was identified and completely sequenced. Southern blot analysis confirmed the presence of two copies of the TS gene in the MaHV-1 genome as expected. Computer analysis of the TS ORF showed it was 948 nucleotides in length. A putative polyadenylation signal was identified 17-22 bp inside the ORF implying a minimal or absent 3' untranslated region. The predicted polypeptide was 316 amino acid residues in length and contained the highly conserved motifs for folate binding and F-dUMP binding, typical of all TS enzymes. Interestingly, MaHV-1 TS polypeptide had highest similarity to the human TS polypeptide (81%) compared to the TS polypeptides of other herpesviruses (72-75%). Immediately upstream of the TS gene, a second ORF of 510 bp, encoding a polypeptide with 170 amino acid residues, was identified. The carboxyl domain of this MaHV-1 polypeptide shared 68% similarity to a 59 amino acid motif of human herpesvirus 1 ICP34.5, identifying it as the MaHV-1 ICP34.5 homologue. This is the first report of a herpesvirus that encodes for both TS and ICP34.5.

(2002). Comments on 'On the estimation of continuous-time transfer functions' by L. Wang and P. Gawthrop. International Journal of Control: Vol. 75, No. 9, pp. 693-697.

The paper outlines an optimal, maximum likelihood formulation of the continuous-time transfer function estimation problem and shows how the Refined Instrumental Variable (RIVC) algorithm provides an iterative solution to this problem. With the help of a simulation example, it then compares and contrasts the performance of the RIVC algorithm with that of two other, sub-optimal, continuous-time transfer function estimation procedures that have been suggested more recently.

A control system design procedure is proposed for a widely applicable class of discrete-time, non-linear systems in which the system nonlinearities are incorporated into a linear model structure in the form of State-Dependent Parameter (SDP) functions. The identification and estimation of both non-parametric and parametric SDP models is discussed briefly. The SDP NMSS model structure is defined, and the SDP Proportional-Integral-Plus (PIP) control algorithm is derived using an optimal LQ technique. The practical utility of the design methodology is illustrated by numerical example.

The use of small-scale process descriptions at larger scales has been identified as a major problem in hydrological modelling. An alternative is to use measurements taken at a larger scale, such as remote sensing data, and to develop process descriptions at that scale. A hypothetical catchment area, or 'virtual hydrological reality' dataset has been generated to explore the value of different types of data in model structure and parameter identification. Though only an approximation to the natural responses of the catchment, this virtual reality can be used to explore the use of observational series that would not normally be available. A data-based mechanistic approach was used to model evapotranspiration from 1 km data, as though they were available from remote sensing. The resulting parameterization is of a similar form to an evaporative fraction model. It is an example of the type of parameterization that could be used to describe the land surface, without estimation of effective parameters from small-scale measurements, and avoiding overparameterization.

The Gemini High-Resolution Optical SpecTrograph (GHOST) is the newest instrument being developed for the Gemini telescopes, in a collaboration between the Australian Astronomical Observatory (AAO), the NRC - Herzberg in Canada and the Australian National University (ANU). We describe the process of design optimisation that utilizes the unique strengths of the new partner, NRC - Herzberg, the design and need for the slit viewing camera system, and we describe a simplification for the lenslet-based slit reformatting. Finally, we out- line the updated project plan, and describe the unique scientific role this instrument will have in an international context, from exoplanets through to the distant Universe.

International audience; The direct identification and estimation of continuous-time models from sampled data is now mature. This paper does not present any new methodology, nor does it compare the performance of existing methods. Its main aim is to discuss the advantages of direct, continuous-time model identification with the help of illustrative examples that are all based on real data from practical applications. Although the specific method of statistical parameter estimation is relatively unimportant in this regard, the latest and most sophisticated time domain identification algorithm that is freely available to the reader is used in these examples in order to ensure that the results reflect the best performance that can be achieved at this time by time-domain identification.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecasting.

This paper demonstrates the use of a novel suite of data-based, recursive modelling techniques for the investigation of biological and other time-series data, including high resolution leaf elongation. The Data-Based Mechanistic (DBM) modelling methodology rejects the common practice of empirical curve fitting for a more objective approach where the model structure is not assumed a priori, but instead is identified directly from the data series in a stochastic form. Further, this novel approach takes advantage of the latest techniques in optimal recursive estimation of non-stationary and non-linear time-series. Here, the utility and ease of use of these techniques is demonstrated in the examination of two time-series of leaf elongation in an expanding leaf of tomato (Lycopersicon esculentum L. cv. Ailsa Craig) growing in a root pressure vessel (RPV). Using this analysis, the component signals of the elongation series are extracted and considered in relation to physiological processes. It is hoped that this paper will encourage the wider use of these new techniques, as well as the associated Data-Based Mechanistic (DBM) modelling strategy, in analytical plant physiology.

This paper presents the result of an investigation into the Proportional Integral Plus (PIP) control of nitrate in the second zone of an activated sludge benchmark. A data-based reduced order model is used as the control model and identified using the Simplified Refined Instrumental Variable (SRIV) identification and estimation algorithm. The PIP control design is based on the Non Minimum State Space (NMSS) form and State Variable Feedback (SVF) methodology. The PIP controller is tested against dynamic load disturbances and compared with the response of a well tuned PI controller.

SUMMARY Motivated by a specific problem concerning the relationship between radar reflectance and rainfall intensity, the paper develops a space–time model for use in environmental monitoring applications. The model is cast as a high dimensional multivariate state space time series model, in which the cross-covariance structure is derived from the spatial context of the component series, in such a way that its interpretation is essentially independent of the particular set of spatial locations at which the data are recorded. We develop algorithms for estimating the parameters of the model by maximum likelihood, and for making spatial predictions of the radar calibration parameters by using realtime computations. We apply the model to data from a weather radar station in Lancashire, England, and demonstrate through empirical validation the predictive performance of the model.

Proportional-integral-plus (PIP) controllers exploit the full power of optimal state variable feedback within a nonminimum state space (NMSS) setting. They are simple to implement and provide a logical extension of conventional proportional-integral/proportional-integral-derivative (PI/PID) controllers, with additional dynamic feedback and input compensators introduced automatically when the process is of greater than first order or has appreciable pure time delays. The present paper provides a tutorial introduction to the NMSS/PIP control design methodology and associated system identification procedure. The latter is based on the utilization of the simplified refined instrumental variable (SRIV) algorithm for the estimation of transfer function models. The practical utility of these techniques is illustrated by their application to the IFAC93 benchmark system, a seventh-order stochastic simulation whose parameters vary randomly within specified ranges. This benchmark provides a good simulation example for tutorial purposes, since it requires the control engineer to work through all the usual design steps, including identification of a low-order control model, control system design, and implementation using a standard programming language, in this case ‘C’. Finally, note that the statistical estimation tools described in the paper have been assembled as a tool-box within the Matlab™ software environment.

The paper outlines how improved estimates of time variable parameters in models of stochastic dynamic systems can be obtained using recursive filtering and fixed interval smoothing techniques, with the associated hyper-parameters optimized by maximum likelihood based on prediction error decomposition. It then shows how, by exploiting special data re-ordering and back-fitting procedures, similar recursive parameter estimation techniques can be utilized to estimate much more rapid State Dependent Parameter (SDP) variations. In this manner, it is possible to identify and estimate a widely applicable class of nonlinear stochastic systems, as illustrated by several examples that include simulated and real data from chaotic processes. Finally, the paper points out that such SDP models can form the basis for new methods of signal processing, automatic control and state estimation for nonlinear stochastic systems.

This paper reports an application of data-based mechanistic (DBM) modelling to the study of reservoir sedimentation. A detailed monitoring programme in the catchment of the Wyresdale Park reservoir was used to calibrate a two component DBM sedimentation model. The first component was a non-linear rainfall to suspended sediment load (SSL, kg s−1) model, the second dealt with sediment routing and the trap efficiency of the reservoir. Daily precipitation data for the period 1911–1996 were used to simulate the sedimentation history of the reservoir. The synthetic accretion sequence evidences the effects of climatic forcing and was compared to lake-bed sediment cores independently dated using 137Cs. The synthetic stack showed general agreement with the observed accretion data. Departures in model performance most likely reflect non-stationarity in the system due to local changes in land use and reservoir regulation.

This paper is concerned with Proportional-Integral-Plus. (PIP) control system design for agricultural buildings. The objective is to regulate the ventilation system to either maintain the comfort and welfare of housed animals or to control the microclimate of a greenhouse, whilst minimising the costs to the farmer. The PIP controller can be interpreted as a logical extension of conventional PI/PID controllers, but with Inherent model-based predictive control action. The paper considers the design of PIP controllers for two test ventilation chambers based at the Katholieke Universiteit Leuven: the first chamber is for controlling temperature and the second for ventilation rate.

Over the last forty years, much attention has been given to the estimation of time variable parameters in models of dynamic systems. This paper outlines how improved estimates of time variable parameters can be obtained using recursive fixed interval smoothing techniques. It then shows how similar techniques can be exploited to estimate state dependent parameter variations and so identify a widely applicable class of nonlinear stochastic systems, which includes chaotic processes.

This paper discusses the Data-Based Mechanistic (DBM) approach to modelling the micro-climate in agricultural buildings. Here, the imperfect mixing processes that dominate the system behaviour during forced ventilation are first modelled objectively, in purely data-based terms, by continuous-time transfer function relationships. In their equivalent differential equation form, however, these models can be interpreted in terms of the Active Mixing Volume (AMV) concept, developed previously at Lancaster in connection with pollution transport in rivers and soils and, latterly, in modelling the micro-climate of horticultural glasshouses. This can be compared with the incomplete mixing and control volume concepts that have been investigated previously at Leuven. The data used in the initial stages of the research project, as described in the paper, have been obtained from a series of planned ventilation experiments carried out in a large instrumented chamber at Leuven. The overall objectives of this collaborative study are twofold: first, to gain a better understanding of the heat transfer and micro-climate dynamics in the chamber; and second, to develop models that can form the basis for the design of optimal Proportional-Integral-Plus (PIP-LQ) climate control systems for livestock buildings of a kind used previously for controlling the micro-climate in horticultural glasshouses. Although not specifically directed at glasshouse systems, the techniques described in the paper can be applied straightforwardly within a glasshouse context.

The data-based mechanistic (DBM) modelling methodology is applied to the study of reservoir sedimentation. A lumped-parameter, discrete-time model has been developed which directly relates rainfall to suspended sediment load (SSL) at the reservoir outflow from the two years of measured data at Wyresdale Park Reservoir (Lancashire, UK). This nonlinear DBM model comprises two components: a rainfall to SSL model and a second model, relating the SSL at the reservoir inflow to the SSL at the reservoir spillway. Using a daily measured rainfall series as the input, this model is used to reconstruct daily deposition rates between 1911 and 1996. This synthetic sediment accretion sequence is compared with the variations in sand content within sediment cores collected from the reservoir floor. These profiles show good general agreement, reflecting the importance of low reoccurrence, high magnitude events. This preliminary study highlights the potential of this DBM approach, which could be readily applied to other sites.

This paper shows how proportional-integral-plus linear-quadratic (PIP-LQ) control, based on non-minimal state space (NMSS) control system design, can be constrained to yield exactly the same control algorithm as both generalized predictive control (GPC) and standard, minimal state, linear quadratic gaussian (LQG) design methods. However, while NMSS includes these other approaches as special cases, it is less constrained and so more flexible in general terms: for example, while PIP-LQ has the simplicity of GPC, it is formulated like LQG in the powerful context of state variable feedback (SVF) control, which allows for ready access to modern robust control methods. Furthermore, the paper suggests that the NMSS approach provides the greater design freedom, with a wider range of possible LQ solutions than its minimal state space equivalent.

This paper outlines the application of a new data-based mechanistic (DBM) modelling methodology to the characterization of the sediment transmission dynamics in a small upland reservoir, Wyresdale Park, Lancashire. The DBM modelling strategy exploits advanced statistical procedures to infer the dynamic model structure and its associated parameters directly from the instrumented data, producing a parametrically efficient, continuous time, transfer function model which relates suspended sediment load at the reservoir inflow to the outflow at the event scale. The associated differential equation model parameters have physical attributes which can be interpreted in terms of sediment transmission processes and associated reservoir trap efficiency. Sedigraph analysis suggests that wind-induced resuspension episodically supplies an additional load to the reservoir outlet. The stochastic nature of the DBM model makes it ideal for evaluating the effects of uncertainty through Monte Carlo simulations (MCS) for discharge and sediment transmission.

This paper first explores how movements in Government spending and private capital investment can be related to changes in the unemployment rate of the USA between 1948 and 1988. The resulting model shows that there are very stable dynamic relationships between purely relative measures of these major macro-economic variables over this time; relationships that appear to continue into the post-1988 period and may help in understanding the differences in the investment–unemployment behaviour of the US economy before and after 1988.

This paper describes in detail a flexible approach to nonstationary time series analysis based on a Dynamic Harmonic Regression (DHR) model of the Unobserved Components (UC) type, formulated within a stochastic state space setting. The model is particularly useful for adaptive seasonal adjustment, signal extraction and interpolation over gaps, as well as forecasting or backcasting. The Kalman Filter and Fixed Interval Smoothing algorithms are exploited for estimating the various components, with the Noise Variance Ratio and other hyperparameters in the stochastic state space model estimated by a novel optimization method in the frequency domain. Unlike other approaches of this general type, which normally exploit Maximum Likelihood methods, this optimization procedure is based on a cost function defined in terms of the difference between the logarithmic pseudo-spectrum of the DHR model and the logarithmic autoregressive spectrum of the time series. The cost function not only seems to yield improved convergence characteristics when compared with the alternative ML cost function, but it also has much reduced numerical requirements.

Single leaves of 3-month-old Cedrella odorata seedlings were exposed to a step reduction in the ambient dew point. The resultant time series of dynamic variations in leaf surface water vapour concentration, leaf surface water vapour concentration gradient, transpiration rate and stomatal conductance to water vapour, are analysed using the data-based mechanistic (DBM) modelling methodology of Young (e.g. Young & Lees 1992; Minchin et al. 1996). It is shown that the identified second-order, dynamic model between transpiration rate (as the input) and stomatal conductance (as the output) provides an appropriate, physiologically meaningful, description of the system. In particular, the dynamic relationship between these two variables is remarkably linear and can be resolved in terms of two parallel, first-order, subsystems; a model which complements the results of Cowan (1977) for cotton. The model is also compared with the recently published simulation model of Haefner, Buckley & Mott (1997).

MR imaging is the imaging modality of choice for evaluation of the traumatized lumbar spine, providing critical information for determination of appropriate therapy. It is superior to other modalities for evaluating the supporting ligaments, disc, spinal cord, and the cauda equina. Major fracture patterns are readily discernable. Canal compromise caused by osseous fragments, epidural hemorrhage, or disc fragments is well visualized.

Since the inherent uncertainty associated with most environmental and climatic systems is often acknowledged, it is surprising that most mathematical models of such systems are large, complex and completely deterministic in nature. In this situation, it seems sensible to consider alternative modelling methodologies which overtly acknowledge the often poorly defined nature of such systems and attempt to find simpler, stochastic descriptions which are more appropriate to the often limited data and information base. This paper considers one such approach, Data-based Mechanistic (DBM) modelling, and demonstrates how it can be useful not only for the modelling of environmental and other systems directly from time series data, but also as an approach to the evaluation and simplification of large deterministic simulation models. To achieve these objectives, the DBM approach exploits various methodological tools, including advanced methods of statistical identification and estimation; a particular form of Generalised Sensitivity Analysis based on Monte Carlo Simulation; and Dominant Mode Analysis, the latter involving a new statistical approach to combined model linearisation and order reduction. These various techniques are outlined in the paper and they are applied to the stochastic modelling of water pollution in rivers and the evaluation of nonlinear global carbon cycle models.

In the literature on unobservable component models , three main statistical instruments have been used for signal extraction: fixed interval smoothing (FIS), which derives from Kalman's seminal work on optimal state-space filter theory in the time domain; Wiener-Kolmogorov-Whittle optimal signal extraction (OSE) theory, which is normally set in the frequency domain and dominates the field of classical statistics; and regularization , which was developed mainly by numerical analysts but is referred to as 'smoothing' in the statistical literature (such as smoothing splines, kernel smoothers and local regression). Although some minor recognition of the interrelationship between these methods can be discerned from the literature, no clear discussion of their equivalence has appeared. This paper exposes clearly the interrelationships between the three methods; highlights important properties of the smoothing filters used in signal extraction; and stresses the advantages of the FIS algorithms as a practical sol...

The paper discusses the automatic control of motorway traffic flows utilising ramp metering, i.e. traffic lights on the on-ramp entrances. A multivariable ramp metering system is developed, based on the nonminimal state space (NMSS) approach to control system design using adaptive proportional-integral-plus, linear quadratic (PIP–LQ) optimal controllers. The controller is evaluated on a nonlinear statistical traffic model (STM) simulation of the Amsterdam motorway ring road near the Coen Tunnel.

The paper first describes the identification of a control model for carbon dioxide concentration in an open-top chamber (OTC) used in plant physiology atmospheric change experiments. This model is then employed in the design of a gain-scheduled controller utilising the Proportional-Integral-Plus (PIP) control design methodology developed by Young et al. (1987). The system has been evaluated in a number of field trials, yielding good control, well within the required design specifications.

Mathematical modelling in the natural and engineering sciences is most often dominated by a philosophy of deterministic reductionism. Moreover, many of the `simulation' models that emerge from this approach to modelling are very large and so difficult to identify, estimate (i.e. calibrate) and validate in rigorous statistical terms. In this situation, it seems sensible to consider alternative modelling strategies which overtly acknowledge these data-based modelling difficulties and address the very real problems of calibration and validation associated with the dynamic modelling of complex systems from time series data. This paper outlines a Data-Based Mechanistic (DBM) modelling philosophy which attempts to address some of these problems and illustrates its wide-ranging practical utility through seven examples in areas ranging from the natural environment, through ecology and macro-economics to engineering.

Risk management is evolving into a broader, more general, management role in organisations. This emerging form, called organisation risk management (ORM), seeks to manage all organisation risks in an integrated, comprehensive and strategic manner. The public sector has been somewhat slow to adopt both this view and practice, but changing political, social and economic pressures are forcing public authorities to provide better services with increasingly limited resources; and these pressures, in turn, are heightening the awareness of the value in managing the cost of risk. This paper introduces and develops the ORM concept, and provides a specific application that is a critical issue for the modern police authority — employment risk. In discussing this employment risk, the paper offers both a method for analysing risks generally and a set of risk management principles that apply to the actual management of all organisational risks.

The paper discusses how the conceptual rainfall-runoff model IHACRES (Identification of Hydrographs And Components from Rainfall, Evapotranspiration and Streamflow data) has been extended for application to ephemeral catchments. Such catchments normally operate on a delicate hydrological balance, which is quite often manifested by strong non-linearities in their runoff response, including cessation of flow during the Summer periods of the year. The standard IHACRES model has worked well in temperate, humid catchments, providing reliable predictions on independent streamflow data sets and encapsulating catchment response dynamics in a systematic way. Not surprisingly, however, it does not perform so well for dry climate, low yield catchments and it is necessary to include an additional nonlinear mechanism to account for the ephemeral nature of the discharge. The results of this extended model, when applied to an Australian benchmark catchment, demonstrate significant improvements in simulating runoff during the dry season when significant rainfall events produce little or no discharge.

This paper summarises the results obtained from a stochastic sensitivity study in the area of global carbon cycle modelling. In particular, it outlines how Monte Carlo Simulation (MCS) techniques and associated Generalised Sensitivity Analysis (GSA) have been applied to a modified box-diffusion model. Using parametric and input uncertainty measures derived from independent sources, the analysis shows that the model produces an ensemble response for atmospheric CO2 whose mean evolution is reasonably consistent with that observed over the industrial period (1765 to 1990). However, the situation is more ambiguous in relation to the amplitude distribution of the stochastic realisations: here, there is a larger than expected variance arising from the assumed uncertainty, although this may be due to unavoidable limitations in the assumptions about parameteric uncertainty used in the MCS analysis. The analysis also shows that the a priori model response is less consistent with 13C measurements and not at all consistent with the 14C observations, while GSA suggests that, from over 20 model parameters, only a small number have a statistically significant effect on the model response in 1990. In addition, the results from MCS, using the IPCC (Intergovernmental Panel on Climate Change) Scenario IS92a inputs, demonstrate that the uncertainties in the projections of the CO2 concentrations using this model are significantly higher than those suggested so far using deterministic methods. Finally, the advantages and disadvantages of the stochastic methodology are discussed.

This paper outlines a new approach to the statistical identification and estimation of dynamic models for a widely applicable class of linear and nonlinear systems based on a combination of nonparametric and parametric estimation procedures. The new approach is applied to three systems: a chaotic version of the discrete-time logistic growth equation; a continuous-time, second-order nonlinear system with two feedback nonlinearities; and a predominantly linear but high-order, vibrating cantilever beam.

The proportional-integral-plus (PIP) controller for single-input, single-output, linear systems described by delta (delta) operator models, as presented in a companion paper, is extended to linear, multi-input, multi-output systems. The resulting multivariable PIP control law exploits the full power of state variable feedback control within a non-minimum state-space setting. In this manner, it allows not only for closed-loop pole assignment or linear quadratic optimal control, but also for the simultaneous achievement of multiple objectives, such as the combination of rapid response, smooth input activation and full, or partial, dynamic decoupling. The effectiveness of the design procedure is illustrated by both simulation and practical examples.

Although the state-space, unobserved component approach to forecasting has many advantages, it must be applied carefully in practice and should not be used in an uncritical, ‘black box’ fashion. In particular, such an approach to the modelling and forecasting of Spanish tourism data is inappropriate and leads to misleading conclusions, particularly in relation to the uncritical selection and use of explanatory regression variables.

This paper describes the use of the True Digital Control (TDC) approach (Young, et al ., 1987) in the design, simulation and implementation of Proportional-Integral-Plus (PIP) control of the temperature and relative humidity within an enclosed plant physiology cabinet. Control of the temperature and relative humidity was originally carried out using separate single-input single-output (SISO) controllers running simultaneously (McKenna, et al ., 1997). Due to the high degree of cross coupling which exists between these two channels it is more appropriate to use an optimal linear quadratic (LQ) design philosophy based upon the estimation of a multi-input multi-output (MIMO) model.

The paper will discuss the addition of a plant growth module onto the preexisting micro-climate control model of a Venlo-type glasshouse developed on the Matlab/SIMULINK package (Young et al ., 1994). The existing model, using True Digital Control (TDC) and Proportional-Integral-Plus (PIP) methods (Young et al., 1987; Lees et al ., 1996) monitors and simulates optimal climatic conditions in the glasshouse via control of temperature, relative humidity and carbon dioxide concentration. This is achieved by a complex simulation model which takes account of both internal and external climatic considerations, and produces impressive simulated results which have been validated (Young et al. , 1994).

This paper first presents a statistical ‘data assimilation’ approach to handling the large volume of real time measurements now available from instrumented traffic networks. The dynamic state-space modelling technique employed, herein called the Statistical Traffic Model (STM), transforms on-line roadside measurements of traffic flow into explicit assessments of the current and future state of an inter-urban road network, and provides transport management with a tool for monitoring, prediction and control. The paper then goes on to discuss the application of adaptive Proportional-Integral-Plus (PIP) control systems to a non-linear STM simulation of the Amsterdam ring road.