Real-Time Identification of Time-Varying ARMAX Systems Based on Recursive Update of Its Parameters

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Saied Reza Seydnejad
Keyword(s):  
Real Time ◽  
Basis Functions ◽  
New Method ◽  
Recursive Least Squares ◽  
Time Varying ◽  
Computationally Efficient ◽  
Algorithm Selection ◽  
Varying Parameters ◽  
Original Algorithm ◽  
Time Varying Parameters

A new method for identification of time-varying ARMAX systems is introduced. This method is based on expansion of time-varying parameters of the ARMAX model onto a set of basis functions. A recursive formulation for updating the coefficients of the basis functions of the time-varying parameters of the system is proposed. Similar to non-real-time basis-function methods, the proposed real-time method has the capability of tracking fast changes in the parameters of a time-varying system much better than the standard Kalman and recursive least-squares (RLS) methods. A computationally efficient version of the algorithm is also presented with a small degradation in tracking properties of the original algorithm. Selection of different types of basis functions makes the new method very flexible for different applications.

Real-Time Monitoring of Thermal Response and Life-Time Varying Parameters in Power Modules

2020 ◽  
Vol 56 (5) ◽  
pp. 5279-5291 ◽  
Author(s):  
Christoph H. van der Broeck ◽  
Timothy A. Polom ◽  
Robert D. Lorenz ◽  
Rik W. De Doncker
Keyword(s):  
Real Time ◽  
Thermal Response ◽  
Life Time ◽  
Time Varying ◽  
Real Time Monitoring ◽  
Varying Parameters ◽  
Power Modules ◽  
Time Varying Parameters

Chatter Suppression With Multiple Time-Varying Parameters in Turning

1999 ◽  
Author(s):  
Fulun Yang ◽  
Bi Zhang ◽  
Junyi Yu
Keyword(s):  
Rake Angle ◽  
Machining Process ◽  
New Method ◽  
Multiple Time ◽  
Time Varying ◽  
Varying Parameters ◽  
Chatter Suppression ◽  
Time Varying Parameter ◽  
Time Varying Parameters ◽  
Theoretical Analyses

Abstract This paper presents a new method, multiple time-varying parameter (MTVP) turning for chatter suppression. Compared to the single time-varying parameter (STVP) turning, the new method uses both time-varying spindle speed and time-varying rake angle to suppress chatter. The paper provides theoretical analyses on the MTVP turning and experimental results to justify the analytical results. It compares the effects of chatter suppression between the MTVP and STVP turnings, and discusses the possible mechanisms of chatter suppression. The paper then concludes that the MTVP turning method is more effective in chatter suppression than the STVP turning method because of the combined effect of the multiple time-varying parameters. It is demonstrated that the MTVP turning method can suppress chatter by 80%, and can be applied to suppress all kinds of chatter in a machining process.

scholarly journals Instantaneous Frequency Estimation of Multi-Component Non- Stationary Signals using Fourier Bessel series and Time-Varying Auto Regressive Model

2015 ◽  
Vol 61 (4) ◽  
pp. 365-376
Author(s):  
G. Ravi Shankar Reddy ◽  
Rameshwar Rao
Keyword(s):  
Instantaneous Frequency ◽  
Estimation Error ◽  
Basis Functions ◽  
Time Varying ◽  
Instantaneous Frequency Estimation ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Stationary Signals ◽  
Auto Regressive ◽  
Non Stationary Signal

Abstract In this paper, we propose a novel technique for Instantaneous Frequency (IF) estimation of multi component non stationary signals using Fourier Bessel Series and Time- Varying Auto Regressive (FB-TVAR) model. In the proposed technique, the Fourier-Bessel (FB) expansion decomposes the multi-component non stationary signal into a number of monocomponent signals and TVAR model is used to model each mono-component signal. In TVAR modeling approach the time varying parameters are expanded as a linear combination of basis functions. In this paper, the TVAR parameters are expanded by a discrete cosine basis functions. The maximum likelihood estimation algorithm for model order selection in TVAR models is also discussed. The Instantaneous Frequency (IF) is extracted from the time-varying parameters by calculating the angles of the estimation error filter polynomial roots. The estimation of the TVAR parameters of a multicomponent signal requires the inversion of a large covariance matrix, while the projected technique (FB-TVAR) requires the inversion of a number of comparatively small covariance matrices with better numerical stability properties. Simulation results are presented for Multi component discrete Amplitude and Frequency modulated (AM-FM) signal

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.

Synchronization of Chaotic Systems with Uncertain Time-Varying Parameters

2015 ◽  
Vol 9 (6) ◽  
pp. 568
Author(s):  
Ahmad Al-Jarrah ◽  
Mohammad Ababneh ◽  
Suleiman Bani Hani ◽  
Khalid Al-Widyan
Keyword(s):  
Chaotic Systems ◽  
Time Varying ◽  
Varying Parameters ◽  
Time Varying Parameters ◽  
Uncertain Time
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