Grey-box radial basis function modelling

A fundamental principle in data modelling is to incorporate available a priori information regarding the underlying data generating mechanism into the modelling process. We adopt this principle and consider grey-box radial basis function (RBF) modelling capable of incorporating prior knowledge. Specifically, we show how to explicitly incorporate the two types of prior knowledge: (i) the underlying data generating mechanism exhibits known symmetric property, and (ii) the underlying process obeys a set of given boundary value constraints. The class of efficient orthogonal least squares regression algorithms can readily be applied without any modification to construct parsimonious grey-box RBF models with enhanced generalisation capability.

On combination of SMOTE and particle swarm optimization based radial basis function classifier for imbalanced problems

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

Multi-layer radial basis function networks: an extension to the radial basis function

This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron. It is hoped that by connecting RBFs together in a layered fashion, an equivalent increase in ability can be gained, as is gained from using MLPs instead of single perceptrons. The results of a practical comparison between individual RBFs and MRBF's are also given.

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.

A radial basis function network classifier to maximise leave-one-out mutual information

tWe develop an orthogonal forward selection (OFS) approach to construct radial basis function (RBF)network classifiers for two-class problems. Our approach integrates several concepts in probabilisticmodelling, including cross validation, mutual information and Bayesian hyperparameter fitting. At eachstage of the OFS procedure, one model term is selected by maximising the leave-one-out mutual infor-mation (LOOMI) between the classifier’s predicted class labels and the true class labels. We derive theformula of LOOMI within the OFS framework so that the LOOMI can be evaluated efficiently for modelterm selection. Furthermore, a Bayesian procedure of hyperparameter fitting is also integrated into theeach stage of the OFS to infer the l2-norm based local regularisation parameter from the data. Since eachforward stage is effectively fitting of a one-variable model, this task is very fast. The classifier construc-tion procedure is automatically terminated without the need of using additional stopping criterion toyield very sparse RBF classifiers with excellent classification generalisation performance, which is par-ticular useful for the noisy data sets with highly overlapping class distribution. A number of benchmarkexamples are employed to demonstrate the effectiveness of our proposed approach.

Diagnosing observation error correlations for Doppler radar radial winds in the Met Office UKV model using observation-minus-background and observation-minus-analysis statistics

With the development of convection-permitting numerical weather prediction the efficient use of high resolution observations in data assimilation is becoming increasingly important. The operational assimilation of these observations, such as Dopplerradar radial winds, is now common, though to avoid violating the assumption of un- correlated observation errors the observation density is severely reduced. To improve the quantity of observations used and the impact that they have on the forecast will require the introduction of the full, potentially correlated, error statistics. In this work, observation error statistics are calculated for the Doppler radar radial winds that are assimilated into the Met Office high resolution UK model using a diagnostic that makes use of statistical averages of observation-minus-background and observation-minus-analysis residuals. This is the first in-depth study using the diagnostic to estimate both horizontal and along-beam correlated observation errors. By considering the new results obtained it is found that the Doppler radar radial wind error standard deviations are similar to those used operationally and increase as the observation height increases. Surprisingly the estimated observation error correlation length scales are longer than the operational thinning distance. They are dependent on both the height of the observation and on the distance of the observation away from the radar. Further tests show that the long correlations cannot be attributed to the use of superobservations or the background error covariance matrix used in the assimilation. The large horizontal correlation length scales are, however, in part, a result of using a simplified observation operator.

Breaks in the radial oxygen abundance and corotation radius of three spiral galaxies

The correlation between the breaks in the metallicity distribution and the corotation radius of spiral galaxies has been already advocated in the past and is predicted by a chemodynamical model of our Galaxy that effectively introduces the role of spiral arms in the star formation rate. In this work, we present photometric and spectroscopic observations made with the Gemini Telescope for three of the best candidates of spiral galaxies to have the corotation inside the optical disc: IC 0167, NGC 1042 and NGC 6907. We observed the most intense and well-distributed H ii regions of these galaxies, deriving reliable galactocentric distances and oxygen abundances by applying different statistical methods. From these results, we confirm the presence of variations in the gradients of metallicity of these galaxies that are possibly correlated with the corotation resonance.

A mechanism for the formation of oxygen and iron bimodal radial distribution in the disc of our Galaxy

Recently, it has been proposed that there are two type Ia supernova progenitors: short-lived and long-lived. On the basis of this idea, we develop a theory of a unified mechanism for the formation of the bimodal radial distribution of iron and oxygen in the Galactic disc. The underlying cause for the formation of the fine structure of the radial abundance pattern is the influence of the spiral arms, specifically the combined effect of the corotation resonance and turbulent diffusion. From our modelling, we conclude that in order to explain the bimodal radial distributions simultaneously for oxygen and iron and to obtain approximately equal total iron output from different types of supernovae, the mean ejected iron mass per supernova event should be the same as quoted in the literature if the maximum mass of stars, which eject heavy elements, is 50 M(circle dot). For the upper mass limit of 70 M(circle dot), the production of iron by a type II supernova explosion should increase by about 1.5 times.

New mechanical samples positioning system for irradiations on a radial channel at nuclear research reactor in a full-power continuous operation

This paper describes a new mechanical samples positioning system that allows the safe placement and removal of biological samples for prolonged irradiation, in a nuclear reactor during full-power continuous operation. Also presented herein the materials of construction and operating principles. Additionally, this sample positioning system is compared with an existing pneumatic and automated transfer system, already available at the research reactors. The system consists of a mechanical arm with a claw, which can deliver the samples for irradiations without reactor shutdown. It was installed in the lEA-R1 research reactor at Instituto de Pesquisas Energeticas e Nucleares (IPEN), Sao Paulo, Brazil, and for the past 5 years, the system has successfully operated and allowed the conducting of important experiments. As a result of its introduction, the facility has been in a position to positively respond to the increased demand in studies of biology, medicine, physics, engineering, detector/dosimeter calibrations, etc. It is one example of the appropriated technologies that save energy and resources. (C) 2010 Elsevier Ltd. All rights reserved.

Regulation of Radial Glial Motility by Visual Experience

Radial glia in the developing optic tectum express the key guidance molecules responsible for topographic targeting of retinal axons. However, the extent to which the radial glia are themselves influenced by retinal inputs and visual experience remains unknown. Using multiphoton live imaging of radial glia in the optic tectum of intact Xenopus laevis tadpoles in conjunction with manipulations of neural activity and sensory stimuli, radial glia were observed to exhibit spontaneous calcium transients that were modulated by visual stimulation. Structurally, radial glia extended and retracted many filopodial processes within the tectal neuropil over minutes. These processes interacted with retinotectal synapses and their motility was modulated by nitric oxide (NO) signaling downstream of neuronal NMDA receptor (NMDAR) activation and visual stimulation. These findings provide the first in vivo demonstration that radial glia actively respond both structurally and functionally to neural activity, via NMDAR-dependent NO release during the period of retinal axon ingrowth.

Efeito do transporte radial de partículas na eficiência da geração de corrente por ondas do tipo híbrida inferior em tokamaks

Usamos a teoria quase-linear para estudar os efeitos do transporte radial de partículas na eficiência da geração de corrente por ondas do tipo híbrida inferior (lower hybrid ou LH), em um tokamak modelado como uma lâmina. Nossos resultados numéricos foram obtidos com cinco diferentes modelos do termo de transporte e indicaram que embora a potência absorvida e a corrente gerada possam ser modificadas por efeito do transporte, a proporção de variação dessas quantidades não é muito sensível a uma forma particular do termo de transporte. Na formulação quase-linear utilizada, a evolução no tempo da função distribuição de elétrons, em um dado ponto da geometria de lâmina proposta, ocorre sob a ação de ondas do tipo híbrida inferior, colisões e transporte, e é descrita pela seguinte equação: 8rfe = (8rfehH + (8rfe)COL + (8rfeh . Oterceiro termo pretende demonstrar a natureza e a magnitude dos efeitos de transporte, e é dado pela seguinte forma: (8rfeh = 8s [DT(S) 8sie] , com um coeficiente para difusão espacial dependente de posição. Utilizamos cinco formas totalmente arbitrárias para a dependência de posição, com as quais pretendemos verificar a sensibilidade do processo de geração de corrente a aspectos do termo de difusão.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.