Filtering techniques in self-tuning

In the last few years a state-space formulation has been introduced into self-tuning control. This has not only allowed for a wider choice of possible control actions, but has also provided an insight into the theory underlying—and hidden by—that used in the polynomial description. This paper considers many of the self-tuning algorithms, both state-space and polynomial, presently in use, and by starting from first principles develops the observers which are, effectively, used in each case. At any specific time instant the state estimator can be regarded as taking one of two forms. In the first case the most recently available output measurement is excluded, and here an optimal and conditionally stable observer is obtained. In the second case the present output signal is included, and here it is shown that although the observer is once again conditionally stable, it is no longer optimal. This result is of significance, as many of the popular self-tuning controllers lie in the second, rather than first, category.

Self-tuning proportional, integral and derivative controller based on genetic algorithm least squares

A self-tuning proportional, integral and derivative control scheme based on genetic algorithms (GAs) is proposed and applied to the control of a real industrial plant. This paper explores the improvement in the parameter estimator, which is an essential part of an adaptive controller, through the hybridization of recursive least-squares algorithms by making use of GAs and the possibility of the application of GAs to the control of industrial processes. Both the simulation results and the experiments on a real plant show that the proposed scheme can be applied effectively.

Neurofuzzy state identification using prefiltering

A new state estimator algorithm is based on a neurofuzzy network and the Kalman filter algorithm. The major contribution of the paper is recognition of a bias problem in the parameter estimation of the state-space model and the introduction of a simple, effective prefiltering method to achieve unbiased parameter estimates in the state-space model, which will then be applied for state estimation using the Kalman filtering algorithm. Fundamental to this method is a simple prefiltering procedure using a nonlinear principal component analysis method based on the neurofuzzy basis set. This prefiltering can be performed without prior system structure knowledge. Numerical examples demonstrate the effectiveness of the new approach.

Time series multistep-ahead predictability estimation and ranking

A predictability index was defined as the ratio of the variance of the optimal prediction to the variance of the original time series by Granger and Anderson (1976) and Bhansali (1989). A new simplified algorithm for estimating the predictability index is introduced and the new estimator is shown to be a simple and effective tool in applications of predictability ranking and as an aid in the preliminary analysis of time series. The relationship between the predictability index and the position of the poles and lag p of a time series which can be modelled as an AR(p) model are also investigated. The effectiveness of the algorithm is demonstrated using numerical examples including an application to stock prices.

A concept of approximated densities for efficient nonlinear estimation

This paper presents the theoretical development of a nonlinear adaptive filter based on a concept of filtering by approximated densities (FAD). The most common procedures for nonlinear estimation apply the extended Kalman filter. As opposed to conventional techniques, the proposed recursive algorithm does not require any linearisation. The prediction uses a maximum entropy principle subject to constraints. Thus, the densities created are of an exponential type and depend on a finite number of parameters. The filtering yields recursive equations involving these parameters. The update applies the Bayes theorem. Through simulation on a generic exponential model, the proposed nonlinear filter is implemented and the results prove to be superior to that of the extended Kalman filter and a class of nonlinear filters based on partitioning algorithms.

Oxygen isotope composition of bivalve seasonal growth increments and ambient water in the rivers Rhine and Meuse

The application of oxygen isotope ratios ({delta}18O) from freshwater bivalves as a proxy for river discharge conditions in the Rhine and Meuse rivers is investigated. We compared a dataset of water temperature and water {delta}18O values with a selection of recent shell {delta}18O records for two species of the genus Unio in order to establish: (1) whether differences between the rivers in water {delta}18O values, reflecting river discharge conditions, are recorded in unionid shells; and (2) to what extent ecological parameters influence the accuracy of bivalve shell {delta}18O values as proxies of seasonal, water oxygen isotope conditions in these rivers. The results show that shells from the two rivers differ significantly in {delta}18O values, reflecting different source waters for these two rivers. The seasonal shell {delta}18O records show truncated sinusoidal patterns with narrow peaks and wide troughs, caused by temperature fractionation and winter growth cessation. Interannual growth rate reconstructions show an ontogenetic growth rate decrease. Growth lines in the shell often, but not always, coincide with winter growth cessations in the {delta}18O record, suggesting that growth cessations in the shell {delta}18O records are a better age estimator than counting internal growth lines. Seasonal predicted and measured {delta}18O values correspond well, supporting the hypothesis that these unionids precipitate their shells in oxygen isotopic equilibrium. This means that (sub-) fossil unionids can be used to reconstruct spring-summer river discharge conditions, such as Meuse low-discharge events caused by droughts and Rhine meltwater-influx events caused by melting of snow in the Alps.

Using zero-norm constraint for sparse probability density function estimation

A new sparse kernel probability density function (pdf) estimator based on zero-norm constraint is constructed using the classical Parzen window (PW) estimate as the target function. The so-called zero-norm of the parameters is used in order to achieve enhanced model sparsity, and it is suggested to minimize an approximate function of the zero-norm. It is shown that under certain condition, the kernel weights of the proposed pdf estimator based on the zero-norm approximation can be updated using the multiplicative nonnegative quadratic programming algorithm. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

Evaluation of two "integrated" polarimetric Quantitative Precipitation Estimation (QPE) algorithms at C-band

Two so-called “integrated” polarimetric rate estimation techniques, ZPHI (Testud et al., 2000) and ZZDR (Illingworth and Thompson, 2005), are evaluated using 12 episodes of the year 2005 observed by the French C-band operational Trappes radar, located near Paris. The term “integrated” means that the concentration parameter of the drop size distribution is assumed to be constant over some area and the algorithms retrieve it using the polarimetric variables in that area. The evaluation is carried out in ideal conditions (no partial beam blocking, no ground-clutter contamination, no bright band contamination, a posteriori calibration of the radar variables ZH and ZDR) using hourly rain gauges located at distances less than 60 km from the radar. Also included in the comparison, for the sake of benchmarking, is a conventional Z = 282R1.66 estimator, with and without attenuation correction and with and without adjustment by rain gauges as currently done operationally at Météo France. Under those ideal conditions, the two polarimetric algorithms, which rely solely on radar data, appear to perform as well if not better, pending on the measurements conditions (attenuation, rain rates, …), than the conventional algorithms, even when the latter take into account rain gauges through the adjustment scheme. ZZDR with attenuation correction is the best estimator for hourly rain gauge accumulations lower than 5 mm h−1 and ZPHI is the best one above that threshold. A perturbation analysis has been conducted to assess the sensitivity of the various estimators with respect to biases on ZH and ZDR, taking into account the typical accuracy and stability that can be reasonably achieved with modern operational radars these days (1 dB on ZH and 0.2 dB on ZDR). A +1 dB positive bias on ZH (radar too hot) results in a +14% overestimation of the rain rate with the conventional estimator used in this study (Z = 282R^1.66), a -19% underestimation with ZPHI and a +23% overestimation with ZZDR. Additionally, a +0.2 dB positive bias on ZDR results in a typical rain rate under- estimation of 15% by ZZDR.

Simplified PID self-tuning controller for robotic manipulators

The paper describes a self-tuning adaptive PID controller suitable for use in the control of robotic manipulators. The scheme employs a simple recursive estimator which reduces the computational effort to an acceptable level for many applications in robotics.

Mismatch effects on discrete-time poles

The presence of mismatch between controller and system is considered. A novel discrete-time approach is used to investigate the migration of closed-loop poles when this mismatch occurs. Two forms of state estimator are employed giving rise to several interesting features regarding stability and performance.

Use of the ratio plot in capture-recapture estimation

Statistical graphics are a fundamental, yet often overlooked, set of components in the repertoire of data analytic tools. Graphs are quick and efficient, yet simple instruments of preliminary exploration of a dataset to understand its structure and to provide insight into influential aspects of inference such as departures from assumptions and latent patterns. In this paper, we present and assess a graphical device for choosing a method for estimating population size in capture-recapture studies of closed populations. The basic concept is derived from a homogeneous Poisson distribution where the ratios of neighboring Poisson probabilities multiplied by the value of the larger neighbor count are constant. This property extends to the zero-truncated Poisson distribution which is of fundamental importance in capture–recapture studies. In practice however, this distributional property is often violated. The graphical device developed here, the ratio plot, can be used for assessing specific departures from a Poisson distribution. For example, simple contaminations of an otherwise homogeneous Poisson model can be easily detected and a robust estimator for the population size can be suggested. Several robust estimators are developed and a simulation study is provided to give some guidance on which should be used in practice. More systematic departures can also easily be detected using the ratio plot. In this paper, the focus is on Gamma mixtures of the Poisson distribution which leads to a linear pattern (called structured heterogeneity) in the ratio plot. More generally, the paper shows that the ratio plot is monotone for arbitrary mixtures of power series densities.

Obesity and diabetes, the built environment, and the ‘local’ food economy in the United States, 2007

Obesity and diabetes are increasingly attributed to environmental factors, however, little attention has been paid to the influence of the ‘local’ food economy. This paper examines the association of measures relating to the built environment and ‘local’ agriculture with U.S. county-level prevalence of obesity and diabetes. Key indicators of the ‘local’ food economy include the density of farmers’ markets and the presence of farms with direct sales. This paper employs a robust regression estimator to account for non-normality of the data and to accommodate outliers. Overall, the built environment is associated with the prevalence of obesity and diabetes and a strong local’ food economy may play an important role in prevention. Results imply considerable scope for community-level interventions.

Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis

This paper studies the effects of increasing formality via tax reduction and simplification schemes on micro-firm performance. It uses the 1997 Brazilian SIMPLES program. We develop a simple theoretical model to show that SIMPLES has an impact only on a segment of the micro-firm population, for which the effect of formality on firm performance can be identified, and that can be analyzed along the single dimensional quantiles of the conditional firm revenues. To estimate the effect of formality, we use an econometric approach that compares eligible and non-eligible firms, born before and after SIMPLES in a local interval about the introduction of SIMPLES. We use an estimator that combines both quantile regression and the regression discontinuity identification strategy. The empirical results corroborate the positive effect of formality on microfirms' performance and produce a clear characterization of who benefits from these programs.

A process for analysis of microarray comparative genomics hybridisation studies for bacterial genomes

Background: Microarray based comparative genomic hybridisation (CGH) experiments have been used to study numerous biological problems including understanding genome plasticity in pathogenic bacteria. Typically such experiments produce large data sets that are difficult for biologists to handle. Although there are some programmes available for interpretation of bacterial transcriptomics data and CGH microarray data for looking at genetic stability in oncogenes, there are none specifically to understand the mosaic nature of bacterial genomes. Consequently a bottle neck still persists in accurate processing and mathematical analysis of these data. To address this shortfall we have produced a simple and robust CGH microarray data analysis process that may be automated in the future to understand bacterial genomic diversity. Results: The process involves five steps: cleaning, normalisation, estimating gene presence and absence or divergence, validation, and analysis of data from test against three reference strains simultaneously. Each stage of the process is described and we have compared a number of methods available for characterising bacterial genomic diversity, for calculating the cut-off between gene presence and absence or divergence, and shown that a simple dynamic approach using a kernel density estimator performed better than both established, as well as a more sophisticated mixture modelling technique. We have also shown that current methods commonly used for CGH microarray analysis in tumour and cancer cell lines are not appropriate for analysing our data. Conclusion: After carrying out the analysis and validation for three sequenced Escherichia coli strains, CGH microarray data from 19 E. coli O157 pathogenic test strains were used to demonstrate the benefits of applying this simple and robust process to CGH microarray studies using bacterial genomes.

Distributed classification for pocket data mining

Distributed and collaborative data stream mining in a mobile computing environment is referred to as Pocket Data Mining PDM. Large amounts of available data streams to which smart phones can subscribe to or sense, coupled with the increasing computational power of handheld devices motivates the development of PDM as a decision making system. This emerging area of study has shown to be feasible in an earlier study using technological enablers of mobile software agents and stream mining techniques [1]. A typical PDM process would start by having mobile agents roam the network to discover relevant data streams and resources. Then other (mobile) agents encapsulating stream mining techniques visit the relevant nodes in the network in order to build evolving data mining models. Finally, a third type of mobile agents roam the network consulting the mining agents for a final collaborative decision, when required by one or more users. In this paper, we propose the use of distributed Hoeffding trees and Naive Bayes classifers in the PDM framework over vertically partitioned data streams. Mobile policing, health monitoring and stock market analysis are among the possible applications of PDM. An extensive experimental study is reported showing the effectiveness of the collaborative data mining with the two classifers.