Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

Statistical model identification : dynamical processes and large-scale networks in systems biology

Magdeburg, Univ., Fak. für Verfahrens- und Systemtechnik, Diss., 2014

State-space model identification of a wireless THz network

We model the large scale fading of wireless THz communications links deployed in a metropolitan area taking into account reception through direct line of sight, ground or wall reflection and diffraction. The movement of the receiver in the three dimensions is modelled by an autonomous dynamic linear system in state-space whereas the geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A subspace algorithm in conjunction with polynomial regression is used to identify a Wiener model from time-domain measurements of the field intensity.

MIMO Wiener model identification for large scale fading of wireless mobile communications links

The large scale fading of wireless mobile communications links is modelled assuming the mobile receiver motion is described by a dynamic linear system in state-space. The geometric relations involved in the attenuation and multi-path propagation of the electric field are described by a static non-linear mapping. A Wiener system subspace identification algorithm in conjunction with polynomial regression is used to identify a model from time-domain estimates of the field intensity assuming a multitude of emitters and an antenna array at the receiver end.

Sparse model identification using orthogonal forward regression with basis pursuit and D-optimality

An efficient model identification algorithm for a large class of linear-in-the-parameters models is introduced that simultaneously optimises the model approximation ability, sparsity and robustness. The derived model parameters in each forward regression step are initially estimated via the orthogonal least squares (OLS), followed by being tuned with a new gradient-descent learning algorithm based on the basis pursuit that minimises the l(1) norm of the parameter estimate vector. The model subset selection cost function includes a D-optimality design criterion that maximises the determinant of the design matrix of the subset to ensure model robustness and to enable the model selection procedure to automatically terminate at a sparse model. The proposed approach is based on the forward OLS algorithm using the modified Gram-Schmidt procedure. Both the parameter tuning procedure, based on basis pursuit, and the model selection criterion, based on the D-optimality that is effective in ensuring model robustness, are integrated with the forward regression. As a consequence the inherent computational efficiency associated with the conventional forward OLS approach is maintained in the proposed algorithm. Examples demonstrate the effectiveness of the new approach.

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.

Hammerstein model identification algorithm using Bezier-Bernstein approximation

A new identification algorithm is introduced for the Hammerstein model consisting of a nonlinear static function followed by a linear dynamical model. The nonlinear static function is characterised by using the Bezier-Bernstein approximation. The identification method is based on a hybrid scheme including the applications of the inverse of de Casteljau's algorithm, the least squares algorithm and the Gauss-Newton algorithm subject to constraints. The related work and the extension of the proposed algorithm to multi-input multi-output systems are discussed. Numerical examples including systems with some hard nonlinearities are used to illustrate the efficacy of the proposed approach through comparisons with other approaches.

Experimental model identification of open-frame underwater vehicles

Most of the works published on hydrodynamic parameter identification of open-frame underwater vehicles focus their attention almost exclusively on good coherence between simulated and measured responses, giving less importance to the determination of “actual values” for hydrodynamic parameters. To gain insight into hydrodynamic parameter experimental identification of open-frame underwater vehicles, an experimental identification procedure is proposed here to determine parameters of uncoupled and coupled models. The identification procedure includes: (i) a prior estimation of actual values of the forces/torques applied to the vehicle, (ii) identification of drag parameters from constant velocity tests and (iii) identification of inertia and coupling parameters from oscillatory tests; at this stage, the estimated values of drag parameter obtained in item (ii) are used. The procedure proposed here was used to identify the hydrodynamic parameters of LAURS—an unmanned underwater vehicle developed at the University of São Paulo. The thruster–thruster and thruster–hull interactions and the advance velocity of the vehicle are shown to have a strong impact on the efficiency of thrusters appended to open-frame underwater vehicles, especially for high advance velocities. Results of tests with excitation in 1-DOF and 3-DOF are reported and discussed, showing the feasibility of the developed procedure.

Active Magnetic Bearing System Identification Methods and Rotor Model Updating

Active magnetic bearing is a type of bearing which uses magnetic field to levitate the rotor. These bearings require continuous control of the currents in electromagnets and data from position of the rotor and the measured current from electromagnets. Because of this different identification methods can be implemented with no additional hardware. In this thesis the focus was to implement and test identification methods for active magnetic bearing system and to update the rotor model. Magnetic center calibration is a method used to locate the magnetic center of the rotor. Rotor model identification is an identification method used to identify the rotor model. Rotor model update is a method used to update the rotor model based on identification data. These methods were implemented and tested with a real machine where rotor was levitated with active magnetic bearings and the functionality of the methods was ensured. Methods were developed with further extension in mind and also with the possibility to apply them for different machines with ease.

Development and experimental identification of a biomechanical model of the trunk for functional electrical stimulation control in paraplegia

Objectives. Theoretic modeling and experimental studies suggest that functional electrical stimulation (FES) can improve trunk balance in spinal cord injured subjects. This can have a positive impact on daily life, increasing the volume of bimanual workspace, improving sitting posture, and wheelchair propulsion. A closed loop controller for the stimulation is desirable, as it can potentially decrease muscle fatigue and offer better rejection to disturbances. This paper proposes a biomechanical model of the human trunk, and a procedure for its identification, to be used for the future development of FES controllers. The advantage over previous models resides in the simplicity of the solution proposed, which makes it possible to identify the model just before a stimulation session ( taking into account the variability of the muscle response to the FES). Materials and Methods. The structure of the model is based on previous research on FES and muscle physiology. Some details could not be inferred from previous studies, and were determined from experimental data. Experiments with a paraplegic volunteer were conducted in order to measure the moments exerted by the trunk-passive tissues and artificially stimulated muscles. Data for model identification and validation also were collected. Results. Using the proposed structure and identification procedure, the model could adequately reproduce the moments exerted during the experiments. The study reveals that the stimulated trunk extensors can exert maximal moment when the trunk is in the upright position. In contrast, previous studies show that able-bodied subjects can exert maximal trunk extension when flexed forward. Conclusions. The proposed model and identification procedure are a successful first step toward the development of a model-based controller for trunk FES. The model also gives information on the trunk in unique conditions, normally not observable in able-bodied subjects (ie, subject only to extensor muscles contraction).

Identification of Mechanical Parameters in Mathematical Model of Human Abdominal Fascia

Миглена Г. Кирилова-Донева - Едномерен експеримент на релаксация беше извършен с 14 образци от човешка пъпна фасция. Механичното поведение на фасцията по време на релаксация беше моделирано прилагайки нелинейната теория на Максвел-Гуревич-Рабинович. Параметрите на модела за изследваните образци бяха определени и стойностите им бяха сравнени в зависимост от посоката на натоварване на образците по време на експеримента. Установено бе, че стойностите на началния вискозитет ∗η0 и на параметъра ∗m, който се влияе от скоростта на деформация на материала се изменят в много широки граници не само за образци от различни донори, но и за образци от един донор. В резултат от прилагането на модела бе изчислено изменението на вискозитета и вискозната деформация на материала по време на релаксацията. Бе показано, че изменението на вискозитета и вискозната деформация зависи от посоката на натоварване на образците.

Richards growth model and viability indicators for populations subject to interventions

In this work we study the problem of modeling identification of a population employing a discrete dynamic model based on the Richards growth model. The population is subjected to interventions due to consumption, such as hunting or farming animals. The model identification allows us to estimate the probability or the average time for a population number to reach a certain level. The parameter inference for these models are obtained with the use of the likelihood profile technique as developed in this paper. The identification method here developed can be applied to evaluate the productivity of animal husbandry or to evaluate the risk of extinction of autochthon populations. It is applied to data of the Brazilian beef cattle herd population, and the the population number to reach a certain goal level is investigated.

Model-based analysis of anaerobic acetate uptake by a mixed culture of polyphosphate-accumulating and glycogen-accumulating organisms

An increasing number of studies shows that the glycogen-accumulating organisms (GAOs) can survive and may indeed proliferate under the alternating anaerobic/aerobic conditions found in EBPR systems, thus forming a strong competitor of the polyphosphate-accumulating organisms (PAOs). Understanding their behaviors in a mixed PAO and GAO culture under various operational conditions is essential for developing operating strategies that disadvantage the growth of this group of unwanted organisms. A model-based data analysis method is developed in this paper for the study of the anaerobic PAO and GAO activities in a mixed PAO and GAO culture. The method primarily makes use of the hydrogen ion production rate and the carbon dioxide transfer rate resulting from the acetate uptake processes by PAOs and GAOs, measured with a recently developed titration and off-gas analysis (TOGA) sensor. The method is demonstrated using the data from a laboratory-scale sequencing batch reactor (SBR) operated under alternating anaerobic and aerobic conditions. The data analysis using the proposed method strongly indicates a coexistence of PAOs and GAOs in the system, which was independently confirmed by fluorescent in situ hybridization (FISH) measurement. The model-based analysis also allowed the identification of the respective acetate uptake rates by PAOs and GAOs, along with a number of kinetic and stoichiometric parameters involved in the PAO and GAO models. The excellent fit between the model predictions and the experimental data not involved in parameter identification shows that the parameter values found are reliable and accurate. It also demonstrates that the current anaerobic PAO and GAO models are able to accurately characterize the PAO/GAO mixed culture obtained in this study. This is of major importance as no pure culture of either PAOs or GAOs has been reported to date, and hence the current PAO and GAO models were developed for the interpretation of experimental results of mixed cultures. The proposed method is readily applicable for detailed investigations of the competition between PAOs and GAOs in enriched cultures. However, the fermentation of organic substrates carried out by ordinary heterotrophs needs to be accounted for when the method is applied to the study of PAO and GAO competition in full-scale sludges. (C) 2003 Wiley Periodicals, Inc.

M-estimator, and D-optimality model construction using orthogonal forward regression

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model Structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

Model estimation of cerebral hemodynamics between blood flow and volume changes: A data-based modeling approach

It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV.