Simplified recursive identifier for ARMA processes

Symmetrical behaviour of the covariance matrix and the positive-definite criterion are used to simplify identification of single-input/single-output systems using recursive least squares. Simulation results are obtained and these are compared with ordinary recursive least squares. The adaptive nature of the identifier is verified by varying the system parameters on convergence.

Knowledge transfer processes in PFI/PPP: critical success factors

Successful knowledge transfer is an important process which requires continuous improvement in today’s knowledge-intensive economy. However, improving knowledge transfer processes represents a challenge for construction practitioners due to the complexity of knowledge acquisition, codification and sharing. Although knowledge transfer is context based, understanding the critical success factors can lead to improvements in the transfer process. This paper seeks to identify and evaluate the most significant critical factors for improving knowledge transfer processes in Public Private Partnerships/Private Finance Initiatives (PPP/PFI) projects. Drawing upon a questionnaire survey of 52 construction firms located in the UK, data is analysed using Severity Index (SI) and Coefficient of Variation (COV), to examine and identify these factors in PPP/PFI schemes. The findings suggest that a supportive leadership, participation/commitment from the relevant parties, and good communication between the relevant parties are crucial to improving knowledge transfer processes in PFI schemes. Practitioners, managers and researchers can use the findings to efficiently design performance measures for analysing and improving knowledge transfer processes.

Neurofuzzy design and model construction of nonlinear dynamical processes from data

A common problem in many data based modelling algorithms such as associative memory networks is the problem of the curse of dimensionality. In this paper, a new two-stage neurofuzzy system design and construction algorithm (NeuDeC) for nonlinear dynamical processes is introduced to effectively tackle this problem. A new simple preprocessing method is initially derived and applied to reduce the rule base, followed by a fine model detection process based on the reduced rule set by using forward orthogonal least squares model structure detection. In both stages, new A-optimality experimental design-based criteria we used. In the preprocessing stage, a lower bound of the A-optimality design criterion is derived and applied as a subset selection metric, but in the later stage, the A-optimality design criterion is incorporated into a new composite cost function that minimises model prediction error as well as penalises the model parameter variance. The utilisation of NeuDeC leads to unbiased model parameters with low parameter variance and the additional benefit of a parsimonious model structure. Numerical examples are included to demonstrate the effectiveness of this new modelling approach for high dimensional inputs.

Neurofuzzy control using Kalman filtering state feedback with coloured noise for unknown non-linear processes

This paper presents a controller design scheme for a priori unknown non-linear dynamical processes that are identified via an operating point neurofuzzy system from process data. Based on a neurofuzzy design and model construction algorithm (NeuDec) for a non-linear dynamical process, a neurofuzzy state-space model of controllable form is initially constructed. The control scheme based on closed-loop pole assignment is then utilized to ensure the time invariance and linearization of the state equations so that the system stability can be guaranteed under some mild assumptions, even in the presence of modelling error. The proposed approach requires a known state vector for the application of pole assignment state feedback. For this purpose, a generalized Kalman filtering algorithm with coloured noise is developed on the basis of the neurofuzzy state-space model to obtain an optimal state vector estimation. The derived controller is applied in typical output tracking problems by minimizing the tracking error. Simulation examples are included to demonstrate the operation and effectiveness of the new approach.

Multi-microprocessor control of processes with pure time delay

In 1967 a novel scheme was proposed for controlling processes with large pure time delay (Fellgett et al, 1967) and some of the constituent parts of the scheme were investigated (Swann, 1970; Atkinson et al, 1973). At that time the available computational facilities were inadequate for the scheme to be implemented practically, but with the advent of modern microcomputers the scheme becomes feasible. This paper describes recent work (Mitchell, 1987) in implementing the scheme in a new multi-microprocessor configuration and shows the improved performance it provides compared with conventional three-term controllers.

Processes governing the predictability of the Atlantic meridional overturning circulation in a coupled GCM

The processes that govern the predictability of decadal variations in the North Atlantic meridional overturning circulation (MOC) are investigated in a long control simulation of the ECHO-G coupled atmosphere–ocean model. We elucidate the roles of local stochastic forcing by the atmosphere, and other potential ocean processes, and use our results to build a predictive regression model. The primary influence on MOC variability is found to come from air–sea heat fluxes over the Eastern Labrador Sea. The maximum correlation between such anomalies and the variations in the MOC occurs at a lead time of 2 years, but we demonstrate that the MOC integrates the heat flux variations over a period of 10 years. The corresponding univariate regression model accounts for 74.5% of the interannual variability in the MOC (after the Ekman component has been removed). Dense anomalies to the south of the Greenland-Scotland ridge are also shown to precede the overturning variations by 4–6 years, and provide a second predictor. With the inclusion of this second predictor the resulting regression model explains 82.8% of the total variance of the MOC. This final bivariate model is also tested during large rapid decadal overturning events. The sign of the rapid change is always well represented by the bivariate model, but the magnitude is usually underestimated, suggesting that other processes are also important for these large rapid decadal changes in the MOC.

Nitrogen processes in aquatic ecosystems

Executive summary Nature of the problem (science/management/policy) • Freshwater ecosystems play a key role in the European nitrogen (N) cycle, both as a reactive agent that transfers, stores and processes N loadings from the atmosphere and terrestrial ecosystems, and as a natural environment severely impacted by the increase of these loadings. Approaches • This chapter is a review of major processes and factors controlling N transport and transformations for running waters, standing waters, groundwaters and riparian wetlands. Key findings/state of knowledge • The major factor controlling N processes in freshwater ecosystems is the residence time of water, which varies widely both in space and in time, and which is sensitive to changes in climate, land use and management. • The effects of increased N loadings to European freshwaters include acidification in semi-natural environments, and eutrophication in more disturbed ecosystems, with associated loss of biodiversity in both cases. • An important part of the nitrogen transferred by surface waters is in the form of organic N, as dissolved organic N (DON) and particulate organic N (PON). This part is dominant in semi-natural catchments throughout Europe and remains a significant component of the total N load even in nitrate enriched rivers. • In eutrophicated standing freshwaters N can be a factor limiting or co-limiting biological production, and control of both N and phosphorus (P) loading is oft en needed in impacted areas, if ecological quality is to be restored. Major uncertainties/challenges • The importance of storage and denitrifi cation in aquifers is a major uncertainty in the global N cycle, and controls in part the response of catchments to land use or management changes. In some aquifers, the increase of N concentrations will continue for decades even if efficient mitigation measures are implemented now. • Nitrate retention by riparian wetlands has oft en been highlighted. However, their use for mitigation must be treated with caution, since their effectiveness is difficult to predict, and side effects include increased DON emissions to adjacent open waters, N2O emissions to the atmosphere, and loss of biodiversity. • In fact, the character and specific spatial origins of DON are not fully understood, and similarly the quantitative importance of indirect N2O emissions from freshwater ecosystems as a result of N leaching losses from agricultural soils is still poorly known at the regional scale. • These major uncertainties remain due to the lack of adequate monitoring (all forms of N at a relevant frequency), especially – but not only – in the southern and eastern EU countries. Recommendations (research/policy) • The great variability of transfer pathways, buffering capacity and sensitivity of the catchments and of the freshwater ecosystems calls for site specific mitigation measures rather than standard ones applied at regional to national scale. • The spatial and temporal variations of the N forms, the processes controlling the transport and transformation of N within freshwaters, require further investigation if the role of N in influencing freshwater ecosystem health is to be better understood, underpinning the implementation of the EU Water Framework Directive for European freshwaters.