scholarly journals Full observability and estimation of unknown inputs, states and parameters of nonlinear biological models

2019 ◽  
Vol 16 (156) ◽  
pp. 20190043 ◽  
Author(s):  
Alejandro F. Villaverde ◽  
Nikolaos Tsiantis ◽  
Julio R. Banga
Keyword(s):  
Recent Literature ◽  
State Parameter ◽  
Identification Problem ◽  
Input State ◽  
Biological Models ◽  
Theoretical Possibility ◽  
Model Equations ◽  
Differential Equations Models ◽  
Biological Modelling ◽  
Simultaneous Assessment

In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First, we determine whether it is theoretically possible to infer the states, parameters and inputs, taking only the model equations into account. When this analysis detects deficiencies, we reformulate the model to make it fully observable. Then we move to numerical scenarios and apply an optimization-based technique to estimate the states, parameters and inputs. The results demonstrate the feasibility of an integrated strategy for (i) analysing the theoretical possibility of determining the states, parameters and inputs to a system and (ii) solving the practical problem of actually estimating their values.

Input-state-parameter estimation of structural systems from limited output information

2019 ◽  
Vol 126 ◽  
pp. 711-746 ◽  
Author(s):  
V.K. Dertimanis ◽  
E.N. Chatzi ◽  
S. Eftekhar Azam ◽  
C. Papadimitriou
Keyword(s):  
Parameter Estimation ◽  
State Parameter ◽  
Structural Systems ◽  
Input State ◽  
Limited Output ◽  
Output Information

scholarly journals Finding and Breaking Lie Symmetries: Implications for Structural Identifiability and Observability in Biological Modelling

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 469 ◽  
Author(s):  
Gemma Massonis ◽  
Alejandro F. Villaverde
Keyword(s):  
Nonlinear Models ◽  
Rational Functions ◽  
Lie Symmetries ◽  
Structural Identifiability ◽  
Unknown Parameters ◽  
Identifiability Analysis ◽  
Model Equations ◽  
Rational Expressions ◽  
Model Reformulation ◽  
Biological Modelling

A dynamic model is structurally identifiable (respectively, observable) if it is theoretically possible to infer its unknown parameters (respectively, states) by observing its output over time. The two properties, structural identifiability and observability, are completely determined by the model equations. Their analysis is of interest for modellers because it informs about the possibility of gaining insight into a model’s unmeasured variables. Here we cast the problem of analysing structural identifiability and observability as that of finding Lie symmetries. We build on previous results that showed that structural unidentifiability amounts to the existence of Lie symmetries. We consider nonlinear models described by ordinary differential equations and restrict ourselves to rational functions. We revisit a method for finding symmetries by transforming rational expressions into linear systems. We extend the method by enabling it to provide symmetry-breaking transformations, which allows for a semi-automatic model reformulation that renders a non-observable model observable. We provide a MATLAB implementation of the methodology as part of the STRIKE-GOLDD toolbox for observability and identifiability analysis. We illustrate the use of the methodology in the context of biological modelling by applying it to a set of problems taken from the literature.

Modelling and Simulation of Double Chamber Microbial Fuel Cell: Cell Voltage, Power Density and Temperature Variation with Process Parameters

Green ◽  
2013 ◽  
Vol 3 (3-4) ◽  
Author(s):  
Ravi Shankar ◽  
Prasenjit Mondal ◽  
Shri Chand
Keyword(s):  
Fuel Cell ◽  
Microbial Fuel Cell ◽  
Power Density ◽  
Recent Literature ◽  
Cell Voltage ◽  
Membrane Thickness ◽  
The Difference ◽  
State Models ◽  
Model Equations ◽  
Double Chamber

AbstractIn the present paper steady state models of a double chamber glucose glutamic acid microbial fuel cell (GGA-MFC) under continuous operation have been developed and solved using Matlab 2007 software. The experimental data reported in a recent literature has been used for the validation of the models. The present models give prediction on the cell voltage and cell power density with 19–44% errors, which is less (up to 20%) than the errors on the prediction of cell voltage made in some recent literature for the same MFC where the effects of the difference in pH and ionic conductivity between anodic and cathodic solutions on cell voltage were not incorporated in model equations. It also describes the changes in anodic and cathodic chamber temperature due to the increase in substrate concentration and cell current density. Temperature profile across the membrane thickness has also been studied.

scholarly journals Identifying the Real Effects of Zombie Lending

2020 ◽  
Vol 9 (3) ◽  
pp. 569-592 ◽  
Author(s):  
Fabiano Schivardi ◽  
Enrico Sette ◽  
Guido Tabellini
Keyword(s):  
Firm Performance ◽  
Recent Literature ◽  
Identification Problem ◽  
Policy Response ◽  
The Real ◽  
Real Effects ◽  
Credit Guarantees ◽  
Editorial Decision ◽  
Zombie Lending ◽  
Mechanical Consequence

Abstract The policy response to COVID-19 includes the provision of credit guarantees to firms, a provision that may generate zombie lending. According to the recent literature, the relative performance of healthy firms deteriorates as the fraction of zombies increases. We argue that this literature faces a serious identification problem, because firm performance is often used to define zombies (sometimes implicitly). We show that, under general conditions for the distribution of firm performance, the correlation between healthy firm performance and zombies is a mechanical consequence of an increase in the fraction of zombies with no causal meaning. (JEL E44, G21) Received June 2, 2020; editorial decision June 23, 2020 by Editor Uday Rajan.

Learning from mistakes: Online updating for deep learning models.

2020 ◽  
Author(s):  
Daniel Klotz ◽  
Frederik Kratzert ◽  
Alden K. Sampson ◽  
Günter Klambauer ◽  
Sepp Hochreiter ◽  
...  
Keyword(s):  
Data Assimilation ◽  
State Parameter ◽  
Large Data ◽  
Simulation Models ◽  
Data Driven ◽  
Input State ◽  
Large Set ◽  
Rainfall Runoff ◽  
Forecasting Models ◽  
System States

<p>Accurate streamflow forecasts are important for many operational purposes, like hydropower operation or flood risk management. It is obvious that for data-driven models best prediction performance would be obtained if recent streamflow observations were used as an additional model input. Therefore, there exists a certain imperative which demands to use forecasting models that use discharge signals whenever available.</p><p> </p><p>Forecasting models are, however, not well suited when continuous measurement of discharge can not be guaranteed or for applications in ungauged settings. Regarding the former, missing data can have long lasting repercussions on data-driven models if large data-windows are used for the input. Regarding the latter, data-driven forecast models are not applicable at all. Additionally, we would like to point out that data-driven simulation models need to represent the underlying hydrological processes more closely since the setup explicitly reflects the rainfall-runoff relationship. To conclude, in many contexts, it is more appropriate to use process or simulation models, which do not use discharge as input.</p><p> </p><p>Despite the above mentioned difficulties of forecasting models it would nevertheless be beneficial to integrate, whenever available, past runoff information in simulation models in order to improve their accuracy. To this end, multiple potential approaches and strategies are available. In the context of conceptual or physically based rainfall-runoff models, recent runoff information is usually exploited by data assimilation/updating approaches (e.g. input-, state-, parameter- or output-updating). In this contribution we concentrate on input-updating approaches, since it allows to adjust the system for a forecasting period even if no explicit process can be attached to the system states.</p><p> </p><p>We propose and examine different input-updating techniques for DL-based runoff models that can be used as baselines for future studies on data-assimilation tasks and which can be used with arbitrary differentiable model. To test the proposed approaches, we perform a series of experiments on a large set of basins throughout the continental United States. The results show that even simple updating techniques can strongly improve the forecasting accuracy.   </p><p> </p>

scholarly journals Relating Independent Components to Free-Vibration Modal Responses

2010 ◽  
Vol 17 (2) ◽  
pp. 161-170 ◽  
Author(s):  
S.I. McNeill ◽  
D.C. Zimmerman
Keyword(s):  
Independent Component Analysis ◽  
Free Vibration ◽  
Recent Literature ◽  
Identification Problem ◽  
Component Analysis ◽  
Independent Component ◽  
Modal Identification ◽  
Independent Components ◽  
Vibration Modal ◽  
The Relationship

In recent literature, attempts have been made to apply Independent Component Analysis (ICA) techniques to the modal identification problem. Paramount to this task is establishing a relationship between the source components realized using the suggested ICA technique and modal responses. In this paper, the relationship between independent components and free-vibration modal responses is discussed. Theoretical arguments are presented for responses of undamped systems and arguments are extended to damped responses.

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