An introduction to radial basis functions for system identification: a comparison with other neural network methods

A look is taken at the use of radial basis functions (RBFs), for nonlinear system identification. RBFs are firstly considered in detail themselves and are subsequently compared with a multi-layered perceptron (MLP), in terms of performance and usage.

Belowground biomass functions and expansion factors in high elevation Norway spruce

Biomass allocation to above- and belowground compartments in trees is thought to be affected by growth conditions. To assess the strength of such influences, we sampled six Norway spruce forest stands growing at higher altitudes. Within these stands, we randomly selected a total of 77 Norway spruce trees and measured volume and biomass of stem, above- and belowground stump and all roots over 0.5 cm diameter. A comparison of our observations with models parameterised for lower altitudes shows that models developed for specific conditions may be applicable to other locations. Using our observations, we developed biomass functions (BF) and biomass conversion and expansion factors (BCEF) linking belowground biomass to stem parameters. While both BF and BCEF are accurate in belowground biomass predictions, using BCEF appears more promising as such factors can be readily used with existing forest inventory data to obtain estimates of belowground biomass stock. As an example, we show how BF and BCEF developed for individual trees can be used to estimate belowground biomass at the stand level. In combination with existing aboveground models, our observations can be used to quantify total standing biomass of high altitude Norway spruce stands.

Wiener System identification using B-spline functions with De Boor recursion

A simple and effective algorithm is introduced for the system identification of Wiener system based on the observational input/output data. The B-spline neural network is used to approximate the nonlinear static function in the Wiener system. We incorporate the Gauss-Newton algorithm with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialization scheme. The efficacy of the proposed approach is demonstrated using an illustrative example.

Asymptotic expansions for Taylor coefficients of the composition of two functions

We give an asymptotic expansion for the Taylor coe±cients of L(P(z)) where L(z) is analytic in the open unit disc whose Taylor coe±cients vary `smoothly' and P(z) is a probability generating function. We show how this result applies to a variety of problems, amongst them obtaining the asymptotics of Bernoulli transforms and weighted renewal sequences.

Biomass functions and expansion factors in young Norway spruce (Picea abies [L.] Karst) trees

Roots, stems, branches and needles of 160 Norway spruce trees younger than 10 years were sampled in seven forest stands in central Slovakia in order to establish their biomassfunctions (BFs) and biomassexpansionfactors (BEFs). We tested three models for each biomass pool based on the stem base diameter, tree height and the two parameters combined. BEF values decreased for all spruce components with increasing height and diameter, which was most evident in very young trees under 1 m in height. In older trees, the values of BEFs did tend to stabilise at the height of 3–4 m. We subsequently used the BEFs to calculate dry biomass of the stands based on average stem base diameter and tree height. Total stand biomass grew with increasing age of the stands from about 1.0 Mg ha−1 at 1.5 years to 44.3 Mg ha−1 at 9.5 years. The proportion of stem and branch biomass was found to increase with age, while that of needles was fairly constant and the proportion of root biomass did decrease as the stands grew older.

Application of cylindrical distribution functions to wide-angle X-ray scattering from oriented polymers

A systematic approach is presented for obtaining cylindrical distribution functions (CDF's) of noncrystalline polymers which have been oriented by extension. The scattering patterns and CDF's are also sharpened by the method proposed by Deas and by Ruland. Data from atactic poly(methyl methacrylate) and polystyrene are analysed by these techniques. The methods could also be usefully applied to liquid crystals.

Evaluating the likelihood of the adoption of an animal welfare assessment system in European agri-food supply chains

Purpose – The purpose of this paper is to analyse the likelihood of adoption of a recently designed Welfare Assessment System in agri-food supply chains and the factors affecting the adoption decision. The application is carried out for pig and poultry chains. Design/methodology/approach – This research consisted of two main components: interviews with retailers in pig and poultry supply chains in eight different EU countries to explore their perceptions towards the adoption possibilities of the welfare assessment system; and a conjoint analysis designed to evaluate the perceived adoption likelihood of the assessment system by different Standards Formulating Organisations (SFOs). Findings – Stakeholders were found to be especially concerned about the costs of implementation of the system and how it could, or should, be merged with existing assurance schemes. Another conclusion of the study is that the presence of a strong third independent party supporting the implementation of the welfare assessment system would be the most important influence on the decision whether, or not, to adopt it. Originality/value – This research evaluates the adoption possibilities of a novel Welfare Assessment System and presents the views of different supply chain stakeholders on an adoption of such a system. The main factors affecting the adoption decision are identified and analysed. Contrary to expectations, the costs of adoption of a new welfare assessment system were not considered to be the most important factor affecting the decision of supply chain stakeholders about the adoption of this new welfare system.

System identification of Wiener systems with B-spline functions using De Boor recursion

In this article a simple and effective algorithm is introduced for the system identification of the Wiener system using observational input/output data. The nonlinear static function in the Wiener system is modelled using a B-spline neural network. The Gauss–Newton algorithm is combined with De Boor algorithm (both curve and the first order derivatives) for the parameter estimation of the Wiener model, together with the use of a parameter initialisation scheme. Numerical examples are utilised to demonstrate the efficacy of the proposed approach.

Rhinocetin, a venom-derived integrin-specific antagonist inhibits collagen-induced platelet and endothelial cell functions

Snaclecs are small non-enzymatic proteins present in viper venoms reported to modulate haemostasis of victims through effects on platelets, vascular endothelial and smooth muscle cells. In this study, we have isolated and functionally characterised a snaclec which we named rhinocetin from the venom of West African gaboon viper, Bitis gabonica rhinoceros. Rhinocetin was shown to comprise α and β chains with the molecular masses of 13.5 and 13kDa respectively. Sequence and immunoblot analysis of rhinocetin confirmed this to be a novel snaclec. Rhinocetin inhibited collagen-stimulated activation of human platelets in dose dependent manner, but displayed no inhibitory effects on glycoprotein VI (collagen receptor) selective agonist, CRP-XL-, ADP- or thrombin-induced platelet activation. Rhinocetin antagonised the binding of monoclonal antibodies against the α2 subunit of integrin α2β1 to platelets and coimmunoprecipitation analysis confirmed integrin α2β1 as a target for this venom protein. Rhinocetin inhibited a range of collagen induced platelet functions such as fibrinogen binding, calcium mobilisation, granule secretion, aggregation and thrombus formation. It also inhibited integrin α2β1 dependent functions of human endothelial cells. Together, our data suggest rhinocetin to be a modulator of integrin α2β1 function and thus may provide valuable insights into the role of this integrin in physiological and pathophysiological scenarios including haemostasis, thrombosis and envenomation.

Smoothness of scale functions for spectrally negative Lévy processes

Scale functions play a central role in the fluctuation theory of spectrally negative Lévy processes and often appear in the context of martingale relations. These relations are often require excursion theory rather than Itô calculus. The reason for the latter is that standard Itô calculus is only applicable to functions with a sufficient degree of smoothness and knowledge of the precise degree of smoothness of scale functions is seemingly incomplete. The aim of this article is to offer new results concerning properties of scale functions in relation to the smoothness of the underlying Lévy measure. We place particular emphasis on spectrally negative Lévy processes with a Gaussian component and processes of bounded variation. An additional motivation is the very intimate relation of scale functions to renewal functions of subordinators. The results obtained for scale functions have direct implications offering new results concerning the smoothness of such renewal functions for which there seems to be very little existing literature on this topic.

Likelihood-free estimation of model evidence

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few years, following their integration with Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) in order to better explore the parameter space. They have been applied primarily to estimating the parameters of a given model, but can also be used to compare models. Here we present novel likelihood-free approaches to model comparison, based upon the independent estimation of the evidence of each model under study. Key advantages of these approaches over previous techniques are that they allow the exploitation of MCMC or SMC algorithms for exploring the parameter space, and that they do not require a sampler able to mix between models. We validate the proposed methods using a simple exponential family problem before providing a realistic problem from human population genetics: the comparison of different demographic models based upon genetic data from the Y chromosome.