Control Oriented System Identification for Performance Management in Virtualized Software System

Aryani, Dharma; Wang, Liuping; Patikirikorala, Tharindu

doi:10.3182/20140824-6-za-1003.01100

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…After a detailed analysis of the measured Kv1.1 macroscopic currents to the given input voltage protocol, the Hammerstein–Wiener (HW) model, which is a block-structured system model, was selected. The HW model consists of a linear dynamic subsystem G ( s ) between two static nonlinear elements, as shown in Figure 2 [ 27 , 28 ].…”

Section: Methods and Resultsmentioning

confidence: 99%

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1

Langthaler

Šajić

Rienmüller

et al. 2022

Cells

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The mathematical modeling of ion channel kinetics is an important tool for studying the electrophysiological mechanisms of the nerves, heart, or cancer, from a single cell to an organ. Common approaches use either a Hodgkin–Huxley (HH) or a hidden Markov model (HMM) description, depending on the level of detail of the functionality and structural changes of the underlying channel gating, and taking into account the computational effort for model simulations. Here, we introduce for the first time a novel system theory-based approach for ion channel modeling based on the concept of transfer function characterization, without a priori knowledge of the biological system, using patch clamp measurements. Using the shaker-related voltage-gated potassium channel Kv1.1 (KCNA1) as an example, we compare the established approaches, HH and HMM, with the system theory-based concept in terms of model accuracy, computational effort, the degree of electrophysiological interpretability, and methodological limitations. This highly data-driven modeling concept offers a new opportunity for the phenomenological kinetic modeling of ion channels, exhibiting exceptional accuracy and computational efficiency compared to the conventional methods. The method has a high potential to further improve the quality and computational performance of complex cell and organ model simulations, and could provide a valuable new tool in the field of next-generation in silico electrophysiology.

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Section: Methods and Resultsmentioning

confidence: 99%

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1

Langthaler

Šajić

Rienmüller

et al. 2022

Cells

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“…The updates in Theorem 3 should converge to a local maximum of the marginal likelihood [36], [42], [43]. Then, we can run a final ESS sampler to compute the estimate of the impulse response with (9).…”

Section: Iterative Identification Algorithmmentioning

confidence: 99%

“…The Wiener system is a block-oriented nonlinear model where a linear dynamical system is followed by a static nonlinear function in a cascade composition such as the one presented in Figure 1 [1]. Since their introduction, Wiener systems have been successfully used in many applications, for instance, in chemistry [2]- [5], biology [6], [7], and software systems [8], [9]. The Wiener system has also been used as a building block for control approaches such as Wiener-MPC [10], [11] and Neural network Wiener models [12]- [14].…”

Section: Introductionmentioning

confidence: 99%

Semi-Parametric Kernel-Based Identification of Wiener Systems

Risuleo

Lindsten

Hjalmarsson

2018

2018 IEEE Conference on Decision and Control (CDC)

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We present a technique for kernel-based identification of Wiener systems. We model the impulse response of the linear block with a Gaussian process. The static nonlinearity is modeled with a combination of basis functions. The coefficients of the static nonlinearity are estimated, together with the hyperparameters of the covariance function of the Gaussian process model, using an iterative algorithm based on the expectation-maximization method combined with elliptical slice sampling to sample from the posterior distribution of the impulse response given the data. The same sampling method is then used to find the posterior-mean estimate of the impulse response. We test the proposed algorithm on a benchmark of randomly-generated Wiener systems.

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Data-Driven Predictive Control with Nonlinear Compensation for Performance Management in Virtualized Software System

2017

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Control Oriented System Identification for Performance Management in Virtualized Software System

Cited by 5 publications

References 15 publications

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1

Ion Channel Modeling beyond State of the Art: A Comparison with a System Theory-Based Model of the Shaker-Related Voltage-Gated Potassium Channel Kv1.1

Semi-Parametric Kernel-Based Identification of Wiener Systems

Data-Driven Predictive Control with Nonlinear Compensation for Performance Management in Virtualized Software System

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