Equivalence and identifiability analysis of uncontrolled nonlinear dynamical systems

The problem of parameter identifiability has been considered from different points of view in the case of nonlinear dynamical systems. For analytic systems the standard approach for uncontrolled systems is the Taylor series approach (Pohjanpalo, Math. Biosciences 41 (1978) 21), or the approaches based on differential algebra for polynomial and rational systems. The similarity transformation approach, based on the local state isomorphism theorem, gives a sufficient and necessary condition for global identifiability of nonlinear controlled systems. But it leads only to a necessary condition for identifiability in the case of some uncontrolled systems. Our contribution consists in using the equivalence of systems, based on the straightening out theorem, to analyse the identifiability of uncontrolled systems. From this theory, we state the necessary or sufficient identifiability conditions, some of them depending on the state variable dimension.     

On the completeness of an identifiability algorithm for semi-Markovian models

This paper addresses the problem of identifying causal effects from nonexperimental data in a causal Bayesian network, i.e., a directed acyclic graph that represents causal relationships. The identifiability question asks whether it is possible to compute the probability of some set of (effect) variables given intervention on another set of (intervention) variables, in the presence of non-observable (i.e., hidden or latent) variables. It is well known that the answer to the question depends on the structure of the causal Bayesian network, the set of observable variables, the set of effect variables, and the set of intervention variables. Sound algorithms for identifiability have been proposed, but no complete algorithm is known. We show that the identify algorithm that Tian and Pearl defined for semi-Markovian models (Tian and Pearl 2002, 2002, 2003), an important special case of causal Bayesian networks, is both sound and complete. We believe that this result will prove useful to solve the identifiability question for general causal Bayesian networks.      

Statistical identifiability and convergence evaluation for nonlinear pharmacokinetic models with particle swarm optimization

The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis–Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model.     

State elimination and identifiability of the delay parameter for nonlinear time-delay systems

The identifiability of the delay parameter for nonlinear systems with a single constant time delay is analyzed. We show the existence of input–output equations and relate the identifiability of the delay parameter to their form. Explicit criteria based on rank calculations are formulated. The identifiability of the delay parameter is shown not to be directly related to the well-characterized identifiability/observability of the other system parameters/states.     

Unknown input observer for state affine systems: A necessary and sufficient condition

The problem of designing an unknown input observer for linear systems and its application to fault detection is widely studied in the literature. For nonlinear systems, only subclasses of nonlinear systems and sufficient conditions have been stated. In this paper an unknown input observer design for state affine systems is considered. Based on the geometric approach, a necessary and sufficient condition is given for the existence of an unknown input observer.     

A sufficient condition for optimal control problems with time delays

This paper considers a class of systems modeled by ordinary differential equations which contain a delay in both the state and control variables. A sufficient condition for the optimal control of such systems is presented. The condition is a modification of a sufficient condition for optimal control problems without time delays. The application of the result is illustrated by examples.     

A necessary and sufficient condition for the perspective observability problem

In this paper we derive a necessary and sufficient condition for observing the initial condition of a new class of system known as the perspective system. Such a system has already been applied in the field of computer vision especially in the area of motion estimation problems. Our result generalizes an earlier result by Popov-Belevitch-Hautus on the problem of observing a linear dynamical system.     

Indistinguishability and identifiability of kinetic models for the MurC reaction in peptidoglycan biosynthesis

An important question in Systems Biology is the design of experiments that enable discrimination between two (or more) competing chemical pathway models or biological mechanisms. In this paper analysis is performed between two different models describing the kinetic mechanism of a three-substrate three-product reaction, namely the MurC reaction in the cytoplasmic phase of peptidoglycan biosynthesis. One model involves ordered substrate binding and ordered release of the three products; the competing model also assumes ordered substrate binding, but with fast release of the three products. The two versions are shown to be distinguishable; however, if standard quasi-steady-state assumptions are made distinguishability cannot be determined. Once model structure uniqueness is ensured the experimenter must determine if it is possible to successfully recover rate constant values given the experiment observations, a process known as structural identifiability. Structural identifiability analysis is carried out for both models to determine which of the unknown reaction parameters can be determined uniquely, or otherwise, from the ideal system outputs. This structural analysis forms an integrated step towards the modelling of the full pathway of the cytoplasmic phase of peptidoglycan biosynthesis.     

Bifurcations in nonlinear integral models of biological systems

A bifurcation analysis is suggested for nonlinear integral models of age-distributed biological populations. The analysis shows that the integral model of one-species population with intra-species competition has zero and positive stationary states for some values of a bifurcation parameter. The nontrivial positive stationary state is initially stable and becomes unstable as the parameter grows. The obtained results are discussed and compared with the corresponding results in differential and difference models.     

Global approaches to identifiability testing for linear and nonlinear state space models

Whenever a model is not identifiable, there exist several parameter vectors $\text{θ}$ which yield the same input-output behavior, so that any further interprtation of $\text{θ}$ is questionable. In this paper two methods which can be used to test state-space models for identifiability with a global approach are presented. While the first one is useful for nonlinear and/or time-varying models, the second one is suitable for linear time-invariant models. An appendix provides tools for the solution of he sets of polynomial equations involved in both methods.     

Bayesian nonlinear regression models with scale mixtures of skew-normal distributions: Estimation and case influence diagnostics

The purpose of this paper is to develop a Bayesian analysis for nonlinear regression models under scale mixtures of skew-normal distributions. This novel class of models provides a useful generalization of the symmetrical nonlinear regression models since the error distributions cover both skewness and heavy-tailed distributions such as the skew-t, skew-slash and the skew-contaminated normal distributions. The main advantage of these class of distributions is that they have a nice hierarchical representation that allows the implementation of Markov chain Monte Carlo (MCMC) methods to simulate samples from the joint posterior distribution. In order to examine the robust aspects of this flexible class, against outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. Further, some discussions on the model selection criteria are given. The newly developed procedures are illustrated considering two simulations study, and a real data previously analyzed under normal and skew-normal nonlinear regression models.     

A methodology for control-relevant nonlinear system identification using restricted complexity models

A broadly-applicable, control-relevant system identification methodology for nonlinear restricted complexity models (RCMs) is presented. Control design based on RCMs often leads to controllers which are easy to interpret and implement in real-time. A control-relevant identification method is developed to minimize the degradation in closed-loop performance as a result of RCM approximation error. A two-stage identification procedure is presented. First, a nonlinear ARX model is estimated from plant data using an orthogonal least squares algorithm; a Volterra series model is then generated from the nonlinear ARX model. In the second stage, a RCM with the desired structure is estimated from the Volterra series model through a model reduction algorithm that takes into account closed-loop performance requirements. The effectiveness of the proposed method is illustrated using two chemical reactor examples.     

System identification of nonlinear state-space models

This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the states are required. This problem lends itself perfectly to the particle smoother, which provides arbitrarily good estimates. The maximisation (M) step is solved using standard techniques from numerical optimisation theory. Simulation examples demonstrate the efficacy of our proposed solution.     

A sufficient condition for additive -stability and application to reaction–diffusion models

The matrix A is said to be additively D-stable if A−D remains Hurwitz for all non-negative diagonal matrices D. In reaction–diffusion models, additive D-stability of the matrix describing the reaction dynamics guarantees the stability of the homogeneous steady-state, thus ruling out the possibility of diffusion-driven instabilities. We present a new criterion for additive D-stability using the concept of compound matrices. We first give conditions under which the second additive compound matrix has non-negative off-diagonal entries. We then use this Metzler property of the compound matrix to prove additive D-stability with the help of an additional determinant condition. This result is then applied to investigate the stability of cyclic reaction networks in the presence of diffusion. Finally, a reaction network structure that fails to achieve additive D-stability is exhibited.