Structural identifiability analysis of nonlinear time delayed systems with generalized frequency response functions

summary:In this paper a novel method is proposed for the structural identifiability analysis of nonlinear time delayed systems. It is assumed that all the nonlinearities are analytic functions and the time delays are constant. We consider the joint structural identifiability of models with respect to the ordinary system parameters and time delays by including delays into a unified parameter set. We employ the Volterra series representation of nonlinear dynamical systems and make use of the frequency domain representations of the Volterra kernels, i. e. the Generalized Frequency Response Functions (GFRFs), in order to test the unique computability of the parameters. The advantage of representing nonlinear systems with their GFRFs is that in the frequency domain representation the time delay parameters appear explicitly in the exponents of complex exponential functions from which they can be easily extracted. Since the GFRFs can be symmetrized to be unique, they provide us with an exhaustive summary of the underlying model structure. We use the GFRFs to derive equations for testing structural identifiability. Unique solution of the composed equations with respect to the parameters provides sufficient conditions for structural identifiability. Our method is illustrated on non-linear dynamical system models of different degrees of non-linearities and multiple time delayed terms. Since Volterra series representation can be applied for input-output models, it is also shown that after differential algebraic elimination of unobserved state variables, the proposed method can be suitable for identifiability analysis of a more general class of non-linear time delayed state space models

Computing different realizations of linear dynamical systems with embedding eigenvalue assignment

In this paper we investigate realizability of discrete time linear dynamical systems (LDSs) in fixed state space dimension. We examine whether there exist different Θ = (A,B,C,D) state space realizations of a given Markov parameter sequence Y with fixed B, C and D state space realization matrices. Full observation is assumed in terms of the invertibility of output mapping matrix C. We prove that the set of feasible state transition matrices associated to a Markov parameter sequence Y is convex, provided that the state space realization matrices B, C and D are known and fixed. Under the same conditions we also show that the set of feasible Metzler-type state transition matrices forms a convex subset. Regarding the set of Metzler-type state transition matrices we prove the existence of a structurally unique realization having maximal number of non-zero off-diagonal entries. Using an eigenvalue assignment procedure we propose linear programming based algorithms capable of computing different state space realizations. By using the convexity of the feasible set of Metzler-type state transition matrices and results from the theory of non-negative polynomial systems, we provide algorithms to determine structurally different realization. Computational examples are provided to illustrate structural non-uniqueness of network-based LDSs

Polynomial Time Reachability Analysis in Discrete State Chemical Reaction Networks Obeying Conservation Laws

In this paper the reachability problem of discrete state Chemical Reaction Networks (d-CRNs) is studied. We consider sub-classes of sub-and superconservative d-CRN network structures and prove that the reachability relation can be decided in polynomial time. We make use of the result that in the studied d-CRN sub-classes, the reachability relation is equivalent to the existence of a non-negative integer solution of the d-CRN state equation. The equivalence implies the reformulation of the reachability problem as integer linear programming decision problem. We show that in the studied classes of d-CRN structures, the state equation has a totally unimodular coefficient matrix. As the reachability relation is equivalent to the non-negative integer solution of the state equation, the resulting integer programming decision program can be relaxed to a simple linear program having polynomial time complexity. Hence, in the studied sub-classes of sub and superconservative reaction network structures, the reachability relation can be decided in polynomial time and the number of continuous decision variables is equal to the number of reactions of the d-CRN