Robust nonlinear model identification methods using forward regression

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

A robust nonlinear identification algorithm using PRESS statistic and forward regression

This letter introduces a new robust nonlinear identification algorithm using the Predicted REsidual Sums of Squares (PRESS) statistic and for-ward regression. The major contribution is to compute the PRESS statistic within a framework of a forward orthogonalization process and hence construct a model with a good generalization property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation.

Automatic nonlinear predictive model-construction algorithm using forward regression and the PRESS statistic

An automatic nonlinear predictive model-construction algorithm is introduced based on forward regression and the predicted-residual-sums-of-squares (PRESS) statistic. The proposed algorithm is based on the fundamental concept of evaluating a model's generalisation capability through crossvalidation. This is achieved by using the PRESS statistic as a cost function to optimise model structure. In particular, the proposed algorithm is developed with the aim of achieving computational efficiency, such that the computational effort, which would usually be extensive in the computation of the PRESS statistic, is reduced or minimised. The computation of PRESS is simplified by avoiding a matrix inversion through the use of the orthogonalisation procedure inherent in forward regression, and is further reduced significantly by the introduction of a forward-recursive formula. Based on the properties of the PRESS statistic, the proposed algorithm can achieve a fully automated procedure without resort to any other validation data set for iterative model evaluation. Numerical examples are used to demonstrate the efficacy of the algorithm.

A forward constrained selection algorithm for probabilistic neural network

A new probabilistic neural network (PNN) learning algorithm based on forward constrained selection (PNN-FCS) is proposed. An incremental learning scheme is adopted such that at each step, new neurons, one for each class, are selected from the training samples arid the weights of the neurons are estimated so as to minimize the overall misclassification error rate. In this manner, only the most significant training samples are used as the neurons. It is shown by simulation that the resultant networks of PNN-FCS have good classification performance compared to other types of classifiers, but much smaller model sizes than conventional PNN.

The potential of probiotics as in-feed growth enhancers for swine

Probiotics have enjoyed a surge of popularity in recent years, with many novel applications being proposed. One of the foremost for the agricultural industry is their potential for livestock growth promotion, a subject of special interest since the 2006 EU-wide ban on sub therapeutic levels of in-feed antibiotic growth enhancers. Probiotics work through a number of differing mechanisms, most of which are not, as yet, fully understood. The probiotics interact with the host’s natural gut flora in a complex and varying array of mechanisms, but ultimately work to improve nutrient digestibility and gut health and to suppress the actions of pathogenic bacteria. In conclusion, probiotics can be a useful replacement for in-feed antibiotic growth enhancers. However, care should be taken due to the variability of the size of the effect and the inconsistency of the results in the published literature.

Particle swarm optimization aided orthogonal forward regression for unified data modeling

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

Probability density estimation with tunable kernels using orthogonal forward regression

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

Radial basis function classifier construction using particle swarm optimisation aided orthogonal forward regression

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixednode RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

Effect of feed restriction and supplemental dietary fat on gut peptide and hypothalamic neuropeptide mRNA concentrations in growing wethers

The objectives of the present study were 1) to evaluate the effects of supplemental fat and ME intake on plasma concentrations of glucagon-like peptide-1 (GLP-1), cholecystokinin (CCK), glucose-dependent insulinotropic polypeptide, ghrelin, and oxyntomodulin; and 2) to determine the association of these peptides with DMI and the hypothalamic concentration of mRNA for the following neuropeptides: neuropeptide Y (NPY), agouti-related peptide (AgRP), and proopiomelanocortin (POMC). In a completely randomized block design with a 2 x 2 factorial arrangement of treatments, 32 pens with 2 wethers each were restricted-fed (2.45 Mcal/lamb per day) or offered diets ad libitum (n = 16) with or without 6% supplemental fat (n = 16) for a period of 30 d. Dry matter intake was measured daily. On d 8, 15, 22, and 29, BW was measured before feeding, and 6 h after feeding, blood samples were collected for plasma measurement of insulin, GLP-1, CCK, ghrelin, glucose-dependent insulinotropic polypeptide, oxyntomodulin, glucose, and NEFA concentrations. On d 29, blood was collected 30 min before feeding for the same hormone and metabolite analyses. At the end of the experiment, wethers were slaughtered and the hypothalami were collected to measure concentrations of NPY, AgRP, and POMC mRNA. Offering feed ad libitum (resulting in greater ME intake) increased plasma insulin and NEFA concentrations (P = 0.02 and 0.02, respectively) and decreased hypothalamic mRNA expression of NPY and AgRP (P = 0.07 and 0.02, respectively) compared with the restricted-fed wethers. There was a trend for the addition of dietary fat to decrease DMI (P = 0.12). Addition of dietary fat decreased insulin and glucose concentrations (P < 0.05 and 0.01, respectively) and tended to increase hypothalamic mRNA concentrations for NPY and AgRP (P = 0.07 and 0.11, respectively). Plasma GLP-1 and CCK concentrations increased in wethers offered feed ad libitum compared with restricted-fed wethers, but the response was greater when wethers were offered feed ad libitum and had supplemental fat in the diet (fat x intake interaction, P = 0.04). The prefeeding plasma ghrelin concentration was greater in restricted-fed wethers compared with those offered feed ad libitum, but the concentrations were similar 6 h after feeding (intake x time interaction, P < 0.01). Supplemental dietary fat did not affect (P = 0.22) plasma ghrelin concentration. We conclude that insulin, ghrelin, CCK, and GLP-1 may regulate DMI in sheep by regulating the hypothalamic gene expression of NPY, AgRP, and POMC.

Proposing a way forward: a review of standardisation in the use of age categories and ageing techniques in osteological analysis (2004 to 2009)

This study assesses the current state of adult skeletal age-at-death estimation in biological anthropology through analysis of data published in recent research articles from three major anthropological and archaeological journals (2004–2009). The most commonly used adult ageing methods, age of ‘adulthood’, age ranges and the maximum age reported for ‘mature’ adults were compared. The results showed a wide range of variability in the age at which individuals were determined to be adult (from 14 to 25 years), uneven age ranges, a lack of standardisation in the use of descriptive age categories and the inappropriate application of some ageing methods for the sample being examined. Such discrepancies make comparisons between skeletal samples difficult, while the inappropriate use of some techniques make the resultant age estimations unreliable. At a time when national and even global comparisons of past health are becoming prominent, standardisation in the terminology and age categories used to define adults within each sample is fundamental. It is hoped that this research will prompt discussions in the osteological community (both nationally and internationally) about what defines an ‘adult’, how to standardise the age ranges that we use and how individuals should be assigned to each age category. Skeletal markers have been proposed to help physically identify ‘adult’ individuals.