The Experts below are selected from a list of 22542 Experts worldwide ranked by ideXlab platform
L Dangio - One of the best experts on this subject based on the ideXlab platform.
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daisy a new software tool to test global Identifiability of biological and physiological systems
Computer Methods and Programs in Biomedicine, 2007Co-Authors: Giuseppina Bellu, Stefania Audoly, Maria Pia Saccomani, L DangioAbstract:A priori global Identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but Identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test Identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking Identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter Identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.
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parameter Identifiability of nonlinear systems the role of initial conditions
Automatica, 2003Co-Authors: Maria Pia Saccomani, Stefania Audoly, L DangioAbstract:Identifiability is a fundamental prerequisite for model identification; it concerns uniqueness of the model parameters determined from the input-output data, under ideal conditions of noise-free observations and error-free model structure. In the late 1980s concepts of differential algebra have been introduced in control and system theory. Recently, differential algebra tools have been applied to study the Identifiability of dynamic systems described by polynomial equations. These methods all exploit the characteristic set of the differential ideal generated by the polynomials defining the system. In this paper, it will be shown that the Identifiability test procedures based on differential algebra may fail for systems which are started at specific initial conditions and that this problem is strictly related to the accessibility of the system from the given initial conditions. In particular, when the system is not accessible from the given initial conditions, the ideal I having as generators the polynomials defining the dynamic system may not correctly describe the manifold of the solution. In this case a new ideal that includes all differential polynomials vanishing at the solution of the dynamic system started from the initial conditions should be calculated. An Identifiability test is proposed which works, under certain technical hypothesis, also for systems with specific initial conditions.
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global Identifiability of nonlinear models of biological systems
IEEE Transactions on Biomedical Engineering, 2001Co-Authors: Stefania Audoly, Giuseppina Bellu, L Dangio, Maria Pia Saccomani, Claudio CobelliAbstract:A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global Identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global Identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global Identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global Identifiability of nonlinear models of biological and physiological systems are presented.
Paola Cappellaro - One of the best experts on this subject based on the ideXlab platform.
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quantum hamiltonian Identifiability via a similarity transformation approach and beyond
IEEE Transactions on Automatic Control, 2020Co-Authors: Yuanlong Wang, Daoyi Dong, Hidehiro Yonezawa, Ian R. Petersen, Akira Sone, Paola CappellaroAbstract:The Identifiability of a system is concerned with whether the unknown parameters in the system can be uniquely determined with all the possible data generated by a certain experimental setting. A test of quantum Hamiltonian Identifiability is an important tool to save time and cost when exploring the identification capability of quantum probes and experimentally implementing quantum identification schemes. In this article, we generalize the Identifiability test based on the similarity transformation approach (STA) in classical control theory and extend it to the domain of quantum Hamiltonian identification. We employ the STA to prove the Identifiability of spin-1/2 chain systems with arbitrary dimension assisted by single-qubit probes. We further extend the traditional STA method by proposing a structure preserving transformation (SPT) method for nonminimal systems. We use the SPT method to introduce an indicator for the existence of economic quantum Hamiltonian identification algorithms, whose computational complexity directly depends on the number of unknown parameters (which could be much smaller than the system dimension). Finally, we give an example of such an economic Hamiltonian identification algorithm and perform simulations to demonstrate its effectiveness.
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Quantum Hamiltonian Identifiability via a Similarity Transformation Approach and Beyond
arXiv: Quantum Physics, 2018Co-Authors: Yuanlong Wang, Daoyi Dong, Hidehiro Yonezawa, Ian R. Petersen, Akira Sone, Paola CappellaroAbstract:The Identifiability of a system is concerned with whether the unknown parameters in the system can be uniquely determined with all the possible data generated by a certain experimental setting. A test of quantum Hamiltonian Identifiability is an important tool to save time and cost when exploring the identification capability of quantum probes and experimentally implementing quantum identification schemes. In this paper, we generalize the Identifiability test based on the Similarity Transformation Approach (STA) in classical control theory and extend it to the domain of quantum Hamiltonian identification. We employ STA to prove the Identifiability of spin-1/2 chain systems with arbitrary dimension assisted by single-qubit probes. We further extend the traditional STA method by proposing a Structure Preserving Transformation (SPT) method for non-minimal systems. We use the SPT method to introduce an indicator for the existence of economic quantum Hamiltonian identification algorithms, whose computational complexity directly depends on the number of unknown parameters (which could be much smaller than the system dimension). Finally, we give an example of such an economic Hamiltonian identification algorithm and perform simulations to demonstrate its effectiveness.
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hamiltonian Identifiability assisted by a single probe measurement
Physical Review Letters, 2017Co-Authors: Akira Sone, Paola CappellaroAbstract:We study the Hamiltonian Identifiability of a many-body spin-1/2 system assisted by the measure- ment on a single quantum probe based on the eigensystem realization algorithm (ERA) approach employed in Phys. Rev. Lett. 113, 080401 (2014). We demonstrate a potential application of Grobner basis to the Identifiability test of the Hamiltonian, and provide the necessary experimental resources, such as the lower bound in the number of the required measurement points, the upper bound in total required evolution time, and thus the total measurement time. Focusing on the examples of the Identifiability in the spin chain model with nearest-neighboring interaction, we classify the spin-chain Hamiltonian based on its Identifiability, and provide the control protocols to engineer the nonidentifiable Hamiltonian to be an identifiable Hamiltonian.
Maria Pia Saccomani - One of the best experts on this subject based on the ideXlab platform.
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daisy a new software tool to test global Identifiability of biological and physiological systems
Computer Methods and Programs in Biomedicine, 2007Co-Authors: Giuseppina Bellu, Stefania Audoly, Maria Pia Saccomani, L DangioAbstract:A priori global Identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but Identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test Identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking Identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter Identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.
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parameter Identifiability of nonlinear systems the role of initial conditions
Automatica, 2003Co-Authors: Maria Pia Saccomani, Stefania Audoly, L DangioAbstract:Identifiability is a fundamental prerequisite for model identification; it concerns uniqueness of the model parameters determined from the input-output data, under ideal conditions of noise-free observations and error-free model structure. In the late 1980s concepts of differential algebra have been introduced in control and system theory. Recently, differential algebra tools have been applied to study the Identifiability of dynamic systems described by polynomial equations. These methods all exploit the characteristic set of the differential ideal generated by the polynomials defining the system. In this paper, it will be shown that the Identifiability test procedures based on differential algebra may fail for systems which are started at specific initial conditions and that this problem is strictly related to the accessibility of the system from the given initial conditions. In particular, when the system is not accessible from the given initial conditions, the ideal I having as generators the polynomials defining the dynamic system may not correctly describe the manifold of the solution. In this case a new ideal that includes all differential polynomials vanishing at the solution of the dynamic system started from the initial conditions should be calculated. An Identifiability test is proposed which works, under certain technical hypothesis, also for systems with specific initial conditions.
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global Identifiability of nonlinear models of biological systems
IEEE Transactions on Biomedical Engineering, 2001Co-Authors: Stefania Audoly, Giuseppina Bellu, L Dangio, Maria Pia Saccomani, Claudio CobelliAbstract:A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global Identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global Identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global Identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global Identifiability of nonlinear models of biological and physiological systems are presented.
Stefania Audoly - One of the best experts on this subject based on the ideXlab platform.
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daisy a new software tool to test global Identifiability of biological and physiological systems
Computer Methods and Programs in Biomedicine, 2007Co-Authors: Giuseppina Bellu, Stefania Audoly, Maria Pia Saccomani, L DangioAbstract:A priori global Identifiability is a structural property of biological and physiological models. It is considered a prerequisite for well-posed estimation, since it concerns the possibility of recovering uniquely the unknown model parameters from measured input-output data, under ideal conditions (noise-free observations and error-free model structure). Of course, determining if the parameters can be uniquely recovered from observed data is essential before investing resources, time and effort in performing actual biomedical experiments. Many interesting biological models are nonlinear but Identifiability analysis for nonlinear system turns out to be a difficult mathematical problem. Different methods have been proposed in the literature to test Identifiability of nonlinear models but, to the best of our knowledge, so far no software tools have been proposed for automatically checking Identifiability of nonlinear models. In this paper, we describe a software tool implementing a differential algebra algorithm to perform parameter Identifiability analysis for (linear and) nonlinear dynamic models described by polynomial or rational equations. Our goal is to provide the biological investigator a completely automatized software, requiring minimum prior knowledge of mathematical modelling and no in-depth understanding of the mathematical tools. The DAISY (Differential Algebra for Identifiability of SYstems) software will potentially be useful in biological modelling studies, especially in physiology and clinical medicine, where research experiments are particularly expensive and/or difficult to perform. Practical examples of use of the software tool DAISY are presented. DAISY is available at the web site http://www.dei.unipd.it/~pia/.
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parameter Identifiability of nonlinear systems the role of initial conditions
Automatica, 2003Co-Authors: Maria Pia Saccomani, Stefania Audoly, L DangioAbstract:Identifiability is a fundamental prerequisite for model identification; it concerns uniqueness of the model parameters determined from the input-output data, under ideal conditions of noise-free observations and error-free model structure. In the late 1980s concepts of differential algebra have been introduced in control and system theory. Recently, differential algebra tools have been applied to study the Identifiability of dynamic systems described by polynomial equations. These methods all exploit the characteristic set of the differential ideal generated by the polynomials defining the system. In this paper, it will be shown that the Identifiability test procedures based on differential algebra may fail for systems which are started at specific initial conditions and that this problem is strictly related to the accessibility of the system from the given initial conditions. In particular, when the system is not accessible from the given initial conditions, the ideal I having as generators the polynomials defining the dynamic system may not correctly describe the manifold of the solution. In this case a new ideal that includes all differential polynomials vanishing at the solution of the dynamic system started from the initial conditions should be calculated. An Identifiability test is proposed which works, under certain technical hypothesis, also for systems with specific initial conditions.
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global Identifiability of nonlinear models of biological systems
IEEE Transactions on Biomedical Engineering, 2001Co-Authors: Stefania Audoly, Giuseppina Bellu, L Dangio, Maria Pia Saccomani, Claudio CobelliAbstract:A prerequisite for well-posedness of parameter estimation of biological and physiological systems is a priori global Identifiability, a property which concerns uniqueness of the solution for the unknown model parameters. Assessing a priori global Identifiability is particularly difficult for nonlinear dynamic models. Various approaches have been proposed in the literature but no solution exists in the general case. Here, the authors present a new algorithm for testing global Identifiability of nonlinear dynamic models, based on differential algebra. The characteristic set associated to the dynamic equations is calculated in an efficient way and computer algebra techniques are used to solve the resulting set of nonlinear algebraic equations. The algorithm is capable of handling many features arising in biological system models, including zero initial conditions and time-varying parameters. Examples of usage of the algorithm for analyzing a priori global Identifiability of nonlinear models of biological and physiological systems are presented.
Fariba Ranjbar - One of the best experts on this subject based on the ideXlab platform.
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vertex connectivity for node failure identification in boolean network tomography
Algorithmic Aspects of Wireless Sensor Networks, 2019Co-Authors: Nicola Galesi, Fariba Ranjbar, Michele ZitoAbstract:In this paper we study the node failure identification problem in undirected graphs by means of Boolean Network Tomography. We argue that vertex connectivity plays a central role. We show tight bounds on the maximal Identifiability in a particular class of graphs, the Line of Sight networks. We prove slightly weaker bounds on arbitrary networks. Finally we initiate the study of maximal Identifiability in random networks. We focus on two models: the classical Erdős-Renyi model, and that of Random Regular graphs. The framework proposed in the paper allows a probabilistic analysis of the Identifiability in random networks giving a tradeoff between the number of monitors to place and the maximal Identifiability.
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tight bounds for maximal Identifiability of failure nodes in boolean network tomography
International Conference on Distributed Computing Systems, 2018Co-Authors: Nicola Galesi, Fariba RanjbarAbstract:We study maximal Identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. Under standard assumptions on topologies and on monitors placement, we prove tight upper and lower bounds on the maximal Identifiability of failure nodes for specific classes of network topologies, such as trees, bounded-degree graphs, d-dimensional grids, in both directed and undirected cases. Among other results we prove that directed d-dimensional grids with support n have maximal Identifiability d using nd monitors; and in the undirected case we show that 2d monitors suffice to get Identifiability of d-1. We then study Identifiability under embeddings: we establish relations between maximal Identifiability, embeddability and dimension when network topologies are modelled as DAGs. Through our analysis we also refine and generalize results on limits of maximal Identifiability recently obtained in [12] and [1]. Our results suggest the design of networks over N nodes with maximal Identifiability Ω(√log N) using 2√log N monitors and heuristics to place monitors and edges in a network to boost maximal Identifiability.
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tight bounds for maximal Identifiability of failure nodes in boolean network tomography
arXiv: Data Structures and Algorithms, 2017Co-Authors: Nicola Galesi, Fariba RanjbarAbstract:We study maximal Identifiability, a measure recently introduced in Boolean Network Tomography to characterize networks' capability to localize failure nodes in end-to-end path measurements. Under standard assumptions on topologies and on monitors placement, we prove tight upper and lower bounds on the maximal Identifiability of failure nodes for specific classes of network topologies, such as trees, bounded-degree graphs, $d$-dimensional grids, in both directed and undirected cases. Among other results we prove that directed $d$-dimensional grids with support $n$ have maximal Identifiability $d$ using $nd$ monitors; and in the undirected case we show that $2d$ monitors suffice to get Identifiability of $d-1$. We then study Identifiability under embeddings: we establish relations between maximal Identifiability, embeddability and dimension when network topologies are modelled as DAGs. Through our analysis we also refine and generalize results on limits of maximal Identifiability recently obtained in [11] and [1]. Our results suggest the design of networks over $N$ nodes with maximal Identifiability $\Omega(\sqrt{\log N})$ using $2\sqrt{\log N}$ monitors and heuristics to place monitors and edges in a network to boost maximal Identifiability.
