System identification, time series analysis and forecasting:The Captain Toolbox handbook.

CAPTAIN is a MATLAB compatible toolbox for non stationary time series analysis, system identification, signal processing and forecasting, using unobserved components models, time variable parameter models, state dependent parameter models and multiple input transfer function models. CAPTAIN also includes functions for true digital control

Data-based mechanistic modelling, forecasting, and control.

This article briefly reviews the main aspects of the generic data based mechanistic (DBM) approach to modeling stochastic dynamic systems and shown how it is being applied to the analysis, forecasting, and control of environmental and agricultural systems. The advantages of this inductive approach to modeling lie in its wide range of applicability. It can be used to model linear, nonstationary, and nonlinear stochastic systems, and its exploitation of recursive estimation means that the modeling results are useful for both online and offline applications. To demonstrate the practical utility of the various methodological tools that underpin the DBM approach, the article also outlines several typical, practical examples in the area of environmental and agricultural systems analysis, where DBM models have formed the basis for simulation model reduction, control system design, and forecastin

Data-Based Mechanistic approach to modelling of daily rainfall-flow relationship : a case of the Upper Vaal water management area

Published ArticleAlthough deterministic models still dominate hydrological modelling, there is a notable paradigm shift in catchment response modelling. An approach to represent the daily rainfall-flow (R-F) relationship using Data-Based Mechanistic (DBM) modelling is presented. DBM modelling is an inductive empirical transfer function (TF) approach relating input to output. The study used secondary data from the Department of Water Affairs and Forestry for the Upper Vaal water management area at station C1H007. The R-F model identification and optimisation was implemented in the CAPTAIN Toolbox in MATLAB. The best estimated R-F model was a 2nd order TF with an input lag of one day and R 2T= 56%. In mechanistic interpretation, three parallel flow pathways were discerned; the fast flow, slow flow and the loss component each constituting 49.8%, 24% and 26.2% of the modelled flow respectively. The study demonstrates that the approach adopted herein produces reasonably satisfactory results with a minimum of the readily available catchment data

Detection of embryo mortality and hatch using thermal differences among incubated chicken eggs

Accurate diagnosis of both the stage of embryonic mortality and the hatch process in incubated eggs is a fundamental component in troubleshooting and hatchery management. However, traditional methods disturb incubation, destroy egg samples, risk contamination, are time and labour-intensive and require specialist knowledge and training. Therefore, a new method to accurately detect embryonic mortality and hatching time would be of significant interest for the poultry industry if it could be done quickly, cheaply and be fully integrated into the process. In this study we have continuously measured individual eggshell temperatures and the corresponding micro-environmental air temperatures throughout the 21 days of incubation using standard low-cost temperature sensors. Moreover, we have quantified the thermal interaction between eggs and air by calculating thermal profile changes (temperature drop time, drop length and drop magnitude) that allowed us to detect four categories of egg status (infertile/early death, middle death, late death and hatch) during incubation. A decision tree induction classification model accurately (93.3%) predicted the status of 105 sampled eggs in comparison to the classical hatch residue breakout analyses. With this study we have provided a major contribution to the optimisation of incubation processes by introducing an alternative method for the currently practiced hatch residue breakout analyses.status: publishe

Identification-based Diagnosis of Rainfall ¿Stream Flow Data: the Tinderry Catchment

System identification tools, such as transfer function (TF) model structure identification, recursive estimation, time-varying parameter (TVP) estimation and assessment of data information, are used to evaluate the quality of rainfall-stream flow data from the Tinderry catchment (ACT, Australia) and the timevarying behaviour of the rainfall-stream flow dynamics. For the catchment, given the wide range and the abrupt changes of the single input-single output transfer functions describing different periods or events, we conclude that further investigation of (i) local rainfall effects, (ii) time-varying time delays (travelling time), (iii) time-varying residence times related to the base flow and (iv) occurrence of negative residues is needed. Periods with high and low data information content, for further use in effective parameter estimation procedures, are clearly indicated by the analysis

Linear dynamic harmonic regression

Among the alternative unobserved components formulations within the stochastic state space setting, the dynamic harmonic regression (DHR) model has proven to be particularly useful for adaptive seasonal adjustment, signal extraction, forecasting and back-casting of time series. First, it is shown how to obtain AutoRegressive moving average (ARMA) representations for the DHR components under a generalized random walk setting for the associated stochastic parameters; a setting that includes several well-known random walk models as special cases. Later, these theoretical results are used to derive an alternative algorithm, based on optimization in the frequency domain, for the identification and estimation of DHR models. The main advantages of this algorithm are linearity, fast computational speed, avoidance of some numerical issues, and automatic identification of the DHR model. The signal extraction performance of the algorithm is evaluated using empirical applications and comprehensive Monte Carlo simulation analysis

The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control:Getting Started Guide

The CAPTAIN Toolbox is a collection of MATLAB functions for non-stationary time series analysis, forecasting and control. The toolbox is useful for system identification, signal extraction, interpolation, forecasting, data-based mechanistic modelling and control of a wide range of linear and non-linear stochastic systems. The toolbox consists of three modules, organised into three folders as follows: TVPMOD: Time Variable Parameter (TVP) MODels. For the identification of unobserved components models, with a particular focus on state-dependent and time-variable parameter models (includes the popular dynamic harmonic regression model). RIVSID: Refined Instrumental Variable (RIV) System Identification algorithms. For optimal RIV estimation of multiple-input, continuous- and discrete-time Transfer Function models. TDCONT: True Digital CONTrol (TDC). For multivariable, non-minimal state space control, including pole assignment and optimal design, and with backward shift and delta-operator options. The Toolbox files and Getting Started Guide are free to download. Additional handbooks are also available from the authors

New developments in the CAPTAIN Toolbox for Matlab with case study examples

The CAPTAIN Toolbox is a collection of Matlab algorithmic routines for time series analysis, forecasting and control. It is intended for system identification, signal extraction, interpolation, forecasting and control of a wide range of linear and non-linear stochastic systems across science, engineering and the social sciences. This article briefly reviews the main features of the Toolbox, outlines some recent developments and presents a number of examples that demonstrate the performance of these new routines. The examples range from consideration of global climate data, through to electro-mechanical systems and broiler chicken growth rates. The new version of the Toolbox consists of the following three modules that can be installed independently or together: off-line, time-varying parameter estimation routines for Unobserved Component (UC) modelling and forecasting; Refined Instrumental Variable (RIV) algorithms for the identification and estimation of both discrete and hybrid continuous-time transfer function models; and various routines for Non-Minimal State Space (NMSS) feedback control system design. This new segmented approach is designed to provide new users with a gentler introduction to Toolbox functionality; one that focuses on their preferred application area. It will also facilitate more straightforward incorporation of novel algorithms in the future

The CAPTAIN Toolbox for System Identification, Time Series Analysis, Forecasting and Control: Guide to TVPMOD: Time Variable Parameter Models

The CAPTAIN Toolbox is a collection of MATLAB functions for non-stationary time series analysis, forecasting and control. The toolbox is useful for system identification, signal extraction, interpolation, forecasting, data-based mechanistic modelling and control of a wide range of linear and non-linear stochastic systems. The toolbox consists of three modules, organised into three folders as follows: TVPMOD: Time Variable Parameter (TVP) MODels. For the identification of unobserved components models, with a particular focus on state-dependent and time-variable parameter models (includes the popular dynamic harmonic regression model). RIVSID: Refined Instrumental Variable (RIV) System Identification algorithms. For optimal RIV estimation of multiple-input, continuous- and discrete-time Transfer Function models. TDCONT: True Digital CONTrol (TDC). For multivariable, non-minimal state space control, including pole assignment and optimal design, and with backward shift and delta-operator options. The present document is a guide to the TVPMOD module. The Toolbox files and Getting Started Guide are also available for download