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.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

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.

Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

2015 ◽  
Vol 815 ◽  
pp. 408-412
Author(s):  
M.N. Azuwir ◽  
Mohd Sazli Saad ◽  
Mohd Zakimi Zakaria
Keyword(s):  
Real Time ◽  
Least Squares Method ◽  
Methyl Esters ◽  
Performance Comparison ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Automotive Engine ◽  
Speed Controller ◽  
Self Tuning

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

Geometry Control of the Deposited Layer in a Nonplanar Laser Cladding Process Using a Variable Structure Controller

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Alireza Fathi ◽  
Amir Khajepour ◽  
Mohammad Durali ◽  
Ehsan Toyserkani
Keyword(s):  
Laser Cladding ◽  
Closed Loop ◽  
Least Squares Method ◽  
Sliding Mode ◽  
Scanning Speed ◽  
Recursive Least Squares ◽  
Hammerstein Model ◽  
Model Parameters ◽  
Control Input ◽  
Laser Cladding Process

This paper presents a closed-loop laser cladding process used in nonplanar deposition of desired metallic materials. In the proposed system, the deposited layer geometry is continuously controlled via a sliding mode controller (SMC). The controller, which uses the scanning speed as the control input, is designed based on a parametric Hammerstein model. The model is a parametric dynamic model with several unknown parameters, which are identified experimentally using the recursive least squares method. The designed SMC is robust to all model parameters’ uncertainties and disturbances. The results showed that the tracking accuracy improves and the chattering effect reduces if an integrator on the scanning speed is added to the controller. It was observed that this addition decreases the response speed. The performance of the proposed controllers was verified through the fabrication of several parts made of SS303-L. This verification indicates that the developed closed-loop laser cladding process can reduce stair-step effects as well as production time in rapid prototyping of functional parts created with the adaptive slicing technique.

scholarly journals Functional Outcome of Neurologic-Controlled HAL-Exoskeletal Neurorehabilitation in Chronic Spinal Cord Injury: A Pilot With One Year Treatment and Variable Treatment Frequency

2017 ◽  
Vol 7 (8) ◽  
pp. 735-743 ◽  
Author(s):  
Oliver Jansen ◽  
Thomas A. Schildhauer ◽  
Renate C. Meindl ◽  
Martin Tegenthoff ◽  
Peter Schwenkreis ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Training Session ◽  
Treadmill Walking ◽  
Functional Mobility ◽  
Training Period ◽  
Walk Test ◽  
Timed Up And Go ◽  
Treatment Frequency ◽  
Training Frequency ◽  
Cord Injury

Study Design: Longitudinal prospective study. Objectives: Whether 1-year HAL-BWSTT of chronic spinal cord injured patients can improve independent ambulated mobility further as a function of training frequency, after an initial 3-month training period. Methods: Eight patients with chronic SCI were enrolled. They initially received full standard physical therapy and neurorehabilitation in the acute/subacute posttrauma phase. During this trial, all patients first underwent a daily (5 per week) HAL-BWSTT for 12 weeks. Subsequently, these patients performed a 40-week HAL-BWSTT with a training session frequency of either 1 or 3 to 5 sessions per week. The patients’ functional status including HAL-associated treadmill-walking time, -distance, and -speed with additional analysis of gait pattern, and their independent (without wearing the robot suit) functional mobility improvements, were assessed using the 10-Meter-Walk Test (10MWT), Timed-Up-and-Go Test (TUG) and 6-Minute-Walk Test (6MinWT) on admission, at 6 weeks, 12 weeks, and 1 year after enrollment. The data were analyzed separately for the 2 training frequency subgroups after the initial 12-week training period, which was identical in both groups. Results: During the 1-year follow-up, HAL-associated walking parameters and independent functional improvements were maintained in all the patients. This result held irrespective of the training frequency. Conclusions: Long-term 1-year maintenance of HAL-associated treadmill walking parameters and of improved independent walking abilities after initial 12 weeks of daily HAL-BWSTT is possible and depends mainly on the patients’ ambulatory status accomplished after initial training period. Subsequent regular weekly training, but not higher frequency training, seems to be sufficient to preserve the improvements accomplished.

scholarly journals Modeling the Responses to Resistance Training in an Animal Experiment Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Antony G. Philippe ◽  
Guillaume Py ◽  
François B. Favier ◽  
Anthony M. J. Sanchez ◽  
Anne Bonnieu ◽  
...  
Keyword(s):  
Resistance Training ◽  
Training Session ◽  
Model Parameters ◽  
Mean Power ◽  
Mixed Effects Modeling ◽  
Adaptive Processes ◽  
Systems Models ◽  
And Performance ◽  
Exercise Induced ◽  
Adaptation To Training

The aim of the present study was to test whether systems models of training effects on performance in athletes can be used to explore the responses to resistance training in rats. 11 Wistar Han rats (277 ± 15 g) underwent 4 weeks of resistance training consisting in climbing a ladder with progressive loads. Training amount and performance were computed from total work and mean power during each training session. Three systems models relating performance to cumulated training bouts have been tested: (i) with a single component for adaptation to training, (ii) with two components to distinguish the adaptation and fatigue produced by exercise bouts, and (iii) with an additional component to account for training-related changes in exercise-induced fatigue. Model parameters were fitted using a mixed-effects modeling approach. The model with two components was found to be the most suitable to analyze the training responses (R2=0.53;P<0.001). In conclusion, the accuracy in quantifying training loads and performance in a rodent experiment makes it possible to model the responses to resistance training. This modeling in rodents could be used in future studies in combination with biological tools for enhancing our understanding of the adaptive processes that occur during physical training.

Amplitude-versus-angle inversion based on the L1-norm-based likelihood function and the total variation regularization constraint

Geophysics ◽  
2017 ◽  
Vol 82 (3) ◽  
pp. R173-R182 ◽  
Author(s):  
Chuanhui Li ◽  
Fanchang Zhang
Keyword(s):  
Total Variation ◽  
Least Squares Method ◽  
Likelihood Function ◽  
Low Frequency ◽  
Real Data ◽  
Total Variation Regularization ◽  
Model Parameters ◽  
L1 Norm ◽  
Frequency Components ◽  
Amplitude Versus

In prestack seismic data, outlier errors occur and can negatively influence the outcome of the amplitude-versus-angle (AVA) inversion process. Hence, their effect needs to be minimized during AVA inversion. AVA inversion based on the [Formula: see text]-norm-based likelihood function is highly sensitive to outlier errors. In comparison, AVA inversion based on the [Formula: see text]-norm-based likelihood function is less affected by outlier errors, and for this reason we have used it with the total variation regularization method used as a constraint to invert discontinuities from geologic bodies. To ensure that the inversion results contain low-frequency components, prior information constraints from model parameters are added to the inverse objective function, which is then solved by the iterative reweighted least-squares method. Results of numerical tests and real-data examples from the application of this method indicate that the algorithm is strongly robust against noise, especially abnormal outlier errors, and that the results of the inversion are reasonable.

Constructing and model-fitting receiver operator characteristics using continuous data.

2018 ◽  
Author(s):  
Josephine Ann Urquhart ◽  
Akira O'Connor
Keyword(s):  
Signal Detection ◽  
Least Squares Method ◽  
Model Fitting ◽  
Simulated Data ◽  
Continuous Variable ◽  
Continuous Data ◽  
Model Parameters ◽  
Operating Characteristics ◽  
Unequal Variance ◽  
Discrete Discrimination

Receiver operating characteristics (ROCs) are plots which provide a visual summary of a classifier’s decision response accuracy at varying discrimination thresholds. Typical practice, particularly within psychological studies, involves plotting an ROC from a limited number of discrete thresholds before fitting signal detection parameters to the plot. We propose that additional insight into decision-making could be gained through increasing ROC resolution, using trial-by-trial measurements derived from a continuous variable, in place of discrete discrimination thresholds. Such continuous ROCs are not yet routinely used in behavioural research, which we attribute to issues of practicality (i.e. the difficulty of applying standard ROC model-fitting methodologies to continuous data). Consequently, the purpose of the current article is to provide a documented method of fitting signal detection parameters to continuous ROCs. This method reliably produces model fits equivalent to the unequal variance least squares method of model-fitting (Yonelinas et al., 1998), irrespective of the number of data points used in ROC construction. We present the suggested method in three main stages: I) building continuous ROCs, II) model-fitting to continuous ROCs and III) extracting model parameters from continuous ROCs. Throughout the article, procedures are demonstrated in Microsoft Excel, using an example continuous variable: reaction time, taken from a single-item recognition memory. Supplementary MATLAB code used for automating our procedures is also presented in Appendix B, with a validation of the procedure using simulated data shown in Appendix C.

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