On the existence of a lactate threshold during incremental exercise: a systems analysis

1996 ◽  
Vol 80 (5) ◽  
pp. 1819-1828 ◽  
Author(s):  
M. E. Cabrera ◽  
H. J. Chizeck
Keyword(s):  
Experimental Data ◽  
Threshold Model ◽  
Lactate Concentration ◽  
Incremental Exercise ◽  
Recursive Least Squares ◽  
System Response ◽  
Model Parameters ◽  
Phase System ◽  
Time Varying ◽  
Over Time

The relationship between blood lactate concentration ([La]) and O2 uptake (VO2) during incremental exercise remains controversial: does [La] increase smoothly as a function of VO2 (continuous model), or does it begin to increase abruptly above a particular metabolic rate (threshold model)? The dynamic characteristics of the underlying physiological system are investigated using system identification analysis techniques. A multivariate deterministic time series model of the [La] and VO2 response to incremental changes in work rate was fitted to simulated and experimental data. Time-varying system response parameters were determined through the application of a weighted recursive least squares algorithm. The model, using the identified time-varying parameters, provided a good fit to the data. The variation of these parameters over time was then examined. Two major transitions in the parameters were found to occur at intensity levels equivalent to 53 +/- 8% and 77 +/- 9% maximal VO2 (experimental data). These changes in the model parameters indicate that the best linear dynamic model that fits the observed system behavior has changed. This implies that the system has changed its operation in some way, by altering its structure or by moving to a different operating region. The identified parameter changes over time suggest that the exercise intensity range (from rest to maximal VO2) is divided into three main intensity domains, each with distinct dynamics. Further study of this three-phase system may help in the understanding of the underlying physiological mechanisms that affect the dynamics of [La] and VO2 during exercise.

Pole-Placement Position Control of a Hydraulic Robot Arm

1994 ◽  
Vol 208 (3) ◽  
pp. 149-156 ◽  
Author(s):  
C Özsoy ◽  
A Kural ◽  
A Kuzucu
Keyword(s):  
Discrete Time ◽  
Position Control ◽  
Experimental Studies ◽  
Recursive Least Squares ◽  
Pole Placement ◽  
Model Parameters ◽  
Phase System ◽  
Actual System ◽  
Time Model ◽  
Discrete Time Model

This paper discusses a single-input single-output discrete-time model developed to control joint position of a manipulator design for painting purposes. The input of the discrete-time model is the voltage into the control valve, and the output is the joint displacement. The model parameters are identified by using input-output data collected from the actual system. Recursive least-squares, square root, V-D factorization and variable forgetting factor methods were used for the estimation, and a good match between the model and actual system responses was obtained. The best estimation results were found by the U-D factorization algorithm according to the sum of the squared errors. Since identification results indicate a non-minimum phase system, a pole-placement self-tuning controller is designed for the purpose of joint trajectory control. The control signal computed off-line is applied to the electrohydraulic drive system and the perfect performance of the pole-placement controller is shown by the experimental studies.

Effects of training frequency on the dynamics of performance response to a single training bout

2002 ◽  
Vol 92 (2) ◽  
pp. 572-580 ◽  
Author(s):  
Thierry Busso ◽  
Henri Benoit ◽  
Régis Bonnefoy ◽  
Léonard Feasson ◽  
Jean-René Lacour
Keyword(s):  
Least Squares Method ◽  
Training Session ◽  
Low Frequency ◽  
Recursive Least Squares ◽  
Training Period ◽  
Model Parameters ◽  
Systems Model ◽  
Training Frequency ◽  
Exercise Induced ◽  
Over Time

The aim of this study was to analyze the effect of an increase in training frequency on exercise-induced fatigue by using a systems model with parameters free to vary over time. Six previously untrained subjects undertook a 15-wk training experiment composed of 1) an 8-wk training period with three sessions per week (low-frequency training), 2) 1 wk without training, 3) a 4-wk training period with five sessions per week [high frequency training (HFT)], and 4) 2 wk without training. The systems input ascribed to training loads was computed from interval exercises and expressed in arbitrary units. The systems output ascribed to performance was evaluated three times each week using maximal power sustained over 5 min. The time-varying parameters of the model were estimated by fitting modeled performances to the measured ones using a recursive least squares method. The variations over time in the model parameters showed an increase in magnitude and duration of fatigue induced by a single training bout. The time needed to recover performance after a training session increased from 0.9 ± 2.1 days at the end of low-frequency training to 3.6 ± 2.0 days at the end of HFT. The maximal gain in performance for a given training load decreased during HFT. This study showed that shortening recovery time between training sessions progressively yielded a more persistent fatigue induced by each training.

Recursive identification of time-varying non-linear cascade systems with static input and dynamic output non-linearities

2016 ◽  
Vol 40 (3) ◽  
pp. 896-902 ◽  
Author(s):  
Jozef Vörös
Keyword(s):  
Recursive Least Squares ◽  
Recursive Identification ◽  
Model Description ◽  
Model Parameters ◽  
Time Varying ◽  
Simulation Studies ◽  
Linear Dynamic Systems ◽  
Linear Dynamic ◽  
Cascade Models ◽  
Non Linear

The paper deals with the recursive identification of time-varying non-linear dynamic systems using three-block cascade models with non-linear static, linear dynamic and non-linear dynamic blocks. These models are appropriate for systems with both actuator and sensor non-linearities. Multiple application of a decomposition technique provides special expressions for the corresponding non-linear model description that are linear in parameters. A modified recursive least-squares-based algorithm is used for estimation of the time-varying input polynomial and output backlash parameters. Simulation studies show the feasibility of proposed approach to estimate the model parameters and track their changes.

Estimation of Cerebral Blood Flow From Thermal Measurement

1995 ◽  
Vol 117 (1) ◽  
pp. 74-85 ◽  
Author(s):  
Datong Wei ◽  
G. M. Saidel ◽  
S. C. Jones
Keyword(s):  
Experimental Data ◽  
Nonlinear Least Squares ◽  
Estimation Procedure ◽  
System Response ◽  
Thermal Method ◽  
Model Parameters ◽  
Transfer Model ◽  
Thermal Probe ◽  
Transient Heating ◽  
Least Squares Fit

A thermal method has been developed to quantify continuous perfusion changes with self-calibration. A dynamic, one-dimensional bio-heat transfer model of the thermal probe and tissue describes the system response to either continuous or transient heating. A nonlinear least-squares fit of the model to experimental data yields estimates of the baseline perfusion and other model parameters. With a partial analytical solution of the model, the optimal estimation procedure is two orders of magnitude more efficient than with a total numerical solution of the model system. Experimental data is used to estimate the operating relations between perfusion and the temperature measurement. A new procedure has also been presented to obtain the dynamic response of the system for continuous measurement of perfusion.

scholarly journals Optimal experimental design for mathematical models of haematopoiesis

2021 ◽  
Vol 18 (174) ◽  
pp. 20200729
Author(s):  
Luis Martinez Lomeli ◽  
Abdon Iniguez ◽  
Prasanthi Tata ◽  
Nilamani Jena ◽  
Zhong-Ying Liu ◽  
...  
Keyword(s):  
Experimental Data ◽  
Experimental Design ◽  
Optimal Design ◽  
Latent Variables ◽  
Deterministic Model ◽  
Complex Structure ◽  
Feedback Regulation ◽  
System Response ◽  
Model Parameters ◽  
Underlying Mechanisms

The haematopoietic system has a highly regulated and complex structure in which cells are organized to successfully create and maintain new blood cells. It is known that feedback regulation is crucial to tightly control this system, but the specific mechanisms by which control is exerted are not completely understood. In this work, we aim to uncover the underlying mechanisms in haematopoiesis by conducting perturbation experiments, where animal subjects are exposed to an external agent in order to observe the system response and evolution. We have developed a novel Bayesian hierarchical framework for optimal design of perturbation experiments and proper analysis of the data collected. We use a deterministic model that accounts for feedback and feedforward regulation on cell division rates and self-renewal probabilities. A significant obstacle is that the experimental data are not longitudinal, rather each data point corresponds to a different animal. We overcome this difficulty by modelling the unobserved cellular levels as latent variables. We then use principles of Bayesian experimental design to optimally distribute time points at which the haematopoietic cells are quantified. We evaluate our approach using synthetic and real experimental data and show that an optimal design can lead to better estimates of model parameters.

Modeling of adaptations to physical training by using a recursive least squares algorithm

1997 ◽  
Vol 82 (5) ◽  
pp. 1685-1693 ◽  
Author(s):  
Thierry Busso ◽  
Christian Denis ◽  
Régis Bonnefoy ◽  
André Geyssant ◽  
Jean-René Lacour
Keyword(s):  
Least Squares ◽  
Physical Training ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Time Varying ◽  
Varying Parameters ◽  
Recursive Least Squares Algorithm ◽  
Time Varying Parameters ◽  
Least Squares Algorithm ◽  
Time Invariant

Busso, Thierry, Christian Denis, Régis Bonnefoy, André Geyssant, and Jean-René Lacour. Modeling of adaptations to physical training by using a recursive least squares algorithm. J. Appl. Physiol. 82(5): 1685–1693, 1997.—The present study assesses the usefulness of a systems model with time-varying parameters for describing the responses of physical performance to training. Data for two subjects who undertook a 14-wk training on a cycle ergometer were used to test the proposed model, and the results were compared with a model with time-invariant parameters. Two 4-wk periods of intensive training were separated by a 2-wk period of reduced training and followed by a 4-wk period of reduced training. The systems input ascribed to the training doses was made up of interval exercises and computed in arbitrary units. The systems output was evaluated one to five times per week by using the endurance time at a constant workload. The time-invariant parameters were fitted from actual performances by using the least squares method. The time-varying parameters were fitted by using a recursive least squares algorithm. The coefficients of determination r 2 were 0.875 and 0.879 for the two subjects using the time-varying model, higher than the values of 0.682 and 0.666, respectively, obtained with the time-invariant model. The variations over time in the model parameters resulting from the expected reduction in the residuals appeared generally to account for changes in responses to training. Such a model would be useful for investigating the underlying mechanisms of adaptation and fatigue.

Adaptive control of time-varying non-linear plants by speed-gradient algorithms

2019 ◽  
pp. 37-44 ◽  
Author(s):  
O. P. Tomchina ◽  
D. N. Polyakhov ◽  
O. I. Tokareva ◽  
A. L. Fradkov
Keyword(s):  
Adaptive Control ◽  
Adaptive Systems ◽  
Reference Model ◽  
Maximum Deviation ◽  
Model Parameters ◽  
Time Varying ◽  
System Solution ◽  
Non Linear ◽  
Initial Uncertainty ◽  
Speed Gradient

Introduction: The motion of many real world systems is described by essentially non-linear and non-stationary models. A number of approaches to the control of such plants are based on constructing an internal model of non-stationarity. However, the non-stationarity model parameters can vary widely, leading to more errors. It is only assumed in this paper that the change rate of the object parameters is limited, while the initial uncertainty can be quite large.Purpose: Analysis of adaptive control algorithms for non-linear and time-varying systems with an explicit reference model, synthesized by the speed gradient method.Results: An estimate was obtained for the maximum deviation of a closed-loop system solution from the reference model solution. It is shown that with sufficiently slow changes in the parameters and a small initial uncertainty, the limit error in the system can be made arbitrarily small. Systems designed by the direct approach and systems based on the identification approach are both considered. The procedures for the synthesis of an adaptive regulator and analysis of the synthesized system are illustrated by an example.Practical relevance: The obtained results allow us to build and analyze a broad class of adaptive systems with reference models under non-stationary conditions.

Robustness Over Time-Varying Channels in DNN-HMM ASR Based Human-Robot Interaction

2017 ◽  
Author(s):  
José Novoa ◽  
Jorge Wuth ◽  
Juan Pablo Escudero ◽  
Josué Fredes ◽  
Rodrigo Mahu ◽  
...  
Keyword(s):  
Human Robot Interaction ◽  
Time Varying ◽  
Robot Interaction ◽  
Time Varying Channels ◽  
Over Time

Parameter Determination for Chloride and Tritium Transport in Undisturbed Lysimeters during Steady Flow

1992 ◽  
Vol 23 (2) ◽  
pp. 89-104 ◽  
Author(s):  
Ole H. Jacobsen ◽  
Feike J. Leij ◽  
Martinus Th. van Genuchten
Keyword(s):  
Experimental Data ◽  
Unsaturated Flow ◽  
Transport Model ◽  
Time Moment ◽  
Breakthrough Curves ◽  
Coarse Sand ◽  
Retardation Factor ◽  
Parameter Determination ◽  
Model Parameters ◽  
Water Fraction

Breakthrough curves of Cl and 3H2O were obtained during steady unsaturated flow in five lysimeters containing an undisturbed coarse sand (Orthic Haplohumod). The experimental data were analyzed in terms of the classical two-parameter convection-dispersion equation and a four-parameter two-region type physical nonequilibrium solute transport model. Model parameters were obtained by both curve fitting and time moment analysis. The four-parameter model provided a much better fit to the data for three soil columns, but performed only slightly better for the two remaining columns. The retardation factor for Cl was about 10 % less than for 3H2O, indicating some anion exclusion. For the four-parameter model the average immobile water fraction was 0.14 and the Peclet numbers of the mobile region varied between 50 and 200. Time moments analysis proved to be a useful tool for quantifying the break through curve (BTC) although the moments were found to be sensitive to experimental scattering in the measured data at larger times. Also, fitted parameters described the experimental data better than moment generated parameter values.

scholarly journals Lie-Algebraic Approach for Pricing Zero-Coupon Bonds in Single-Factor Interest Rate Models

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
C. F. Lo
Keyword(s):  
Interest Rate ◽  
Algebraic Approach ◽  
Dynamical Symmetry ◽  
Bond Pricing ◽  
Model Parameters ◽  
Time Varying ◽  
Single Factor ◽  
Bond Price ◽  
Interest Rate Models ◽  
Root Model

The Lie-algebraic approach has been applied to solve the bond pricing problem in single-factor interest rate models. Four of the popular single-factor models, namely, the Vasicek model, Cox-Ingersoll-Ross model, double square-root model, and Ahn-Gao model, are investigated. By exploiting the dynamical symmetry of their bond pricing equations, analytical closed-form pricing formulae can be derived in a straightfoward manner. Time-varying model parameters could also be incorporated into the derivation of the bond price formulae, and this has the added advantage of allowing yield curves to be fitted. Furthermore, the Lie-algebraic approach can be easily extended to formulate new analytically tractable single-factor interest rate models.

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