Nonlinear identification using orthogonal forward regression with nested optimal regularization

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

Generalized convective quasi-equilibrium principle

A generalization of Arakawa and Schubert's convective quasi-equilibrium principle is presented for a closure formulation of mass-flux convection parameterization. The original principle is based on the budget of the cloud work function. This principle is generalized by considering the budget for a vertical integral of an arbitrary convection-related quantity. The closure formulation includes Arakawa and Schubert's quasi-equilibrium, as well as both CAPE and moisture closures as special cases. The formulation also includes new possibilities for considering vertical integrals that are dependent on convective-scale variables, such as the moisture within convection. The generalized convective quasi-equilibrium is defined by a balance between large-scale forcing and convective response for a given vertically-integrated quantity. The latter takes the form of a convolution of a kernel matrix and a mass-flux spectrum, as in the original convective quasi-equilibrium. The kernel reduces to a scalar when either a bulk formulation is adopted, or only large-scale variables are considered within the vertical integral. Various physical implications of the generalized closure are discussed. These include the possibility that precipitation might be considered as a potentially-significant contribution to the large-scale forcing. Two dicta are proposed as guiding physical principles for the specifying a suitable vertically-integrated quantity.

The global-scale impacts of climate change: the QUEST-GSI project

This paper introduces the special issue of Climatic Change on the QUEST-GSI project, a global-scale multi-sectoral assessment of the impacts of climate change. The project used multiple climate models to characterise plausible climate futures with consistent baseline climate and socio-economic data and consistent assumptions, together with a suite of global-scale sectoral impacts models. It estimated impacts across sectors under specific SRES emissions scenarios, and also constructed functions relating impact to change in global mean surface temperature. This paper summarises the objectives of the project and its overall methodology, outlines how the project approach has been used in subsequent policy-relevant assessments of future climate change under different emissions futures, and summarises the general lessons learnt in the project about model validation and the presentation of multi-sector, multi-region impact assessments and their associated uncertainties to different audiences.

Exploring the role the brand plays in the choice of charity by UK volunteers

Charities need to understand why volunteers choose one brand rather than another in order to attract more volunteers to their organisation. There has been considerable academic interest in understanding why people volunteer generally. However, this research explores the more specific question of why a volunteer chooses one charity brand rather than another. It builds on previous conceptualisations of volunteering as a consumption decision. Seen through the lens of the individual volunteer, it considers the under-researched area of the decision-making process. The research adopts an interpretivist epistemology and subjectivist ontology. Qualitative data was collected through depth interviews and analysed using both Means-End Chain (MEC) and Framework Analysis methodology. The primary contribution of the research is to theory: understanding the role of brand in the volunteer decision-making process. It identifies two roles for brand. The first is as a specific reason for choice, an ‘attribute’ of the decision. Through MEC, volunteering for a well-known brand connects directly through to a sense of self, both self-respect but also social recognition by others. All four components of the symbolic consumption construct are found in the data: volunteers choose a well-known brand to say something about themselves. The brand brings credibility and reassurance, it reduces the risk and enables the volunteer to meet their need to make a difference and achieve a sense of accomplishment. The second closely related role for brand is within the process of making the volunteering decision. Volunteers built up knowledge about the charity brands from a variety of brand touchpoints, over time. At the point of decision-making that brand knowledge and engagement becomes relevant, enabling some to make an automatic choice despite the significant level of commitment being made. The research identifies four types of decision-making behaviour. The research also makes secondary contributions to MEC methodology and to the non-profit context. It concludes within practical implications for management practice and a rich agenda for future research.