Topic
System identification
About: System identification is a research topic. Over the lifetime, 21291 publications have been published within this topic receiving 439142 citations.
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TL;DR: A general framework for carrying out perturbation analysis in Stochastic Hybrid Systems (SHS) of arbitrary structure is presented and Infinitesimal Perturbation Analysis (IPA) is used to provide unbiased gradient estimates of performance metrics with respect to various controllable parameters.
136 citations
TL;DR: This chapter reviews two key steps of the model building process, namely parameter estimation (model calibration) and optimal experimental design, and places emphasis on the need for robust global optimization methods for proper solution of these problems.
Abstract: Mathematical models are central in systems biology and provide new ways to understand the function of biological systems, helping in the generation of novel and testable hypotheses, and supporting a rational framework for possible ways of intervention, like in e.g. genetic engineering, drug development or treatment of diseases. Since the amount and quality of experimental 'omics' data continue to increase rapidly, there is great need for methods for proper model building which can handle this complexity. In the present chapter we review two key steps of the model building process, namely parameter estimation (model calibration) and optimal experimental design. Parameter estimation aims to find the unknown parameters of the model which give the best fit to a set of experimental data. Optimal experimental design aims to devise the dynamic experiments which provide the maximum information content for subsequent non-linear model identification, estimation and/or discrimination. We place emphasis on the need for robust global optimization methods for proper solution of these problems, and we present a motivating example considering a cell signalling model.
136 citations
01 Oct 2001
TL;DR: In this article, the authors present a survey of adaptive control for nonlinear control systems with input constrains, including adaptive control of linear systems with unknown time delay and feedback control of processes with hard nonlinearities.
Abstract: 1. New Models and Identification Methods for Backlash and Gear Play.- 2. Adaptive Dead Zone Inverses for Possibly Nonlinear Control Systems.- 3. Deadzone Compensation in Motion Control Systems Using Augmented Multilayer Neural Networks.- 4. On-line Fault Detection, Diagnosis, Isolation and Accommodation of Dynamical Systems with Actuator Failures.- 5. Adaptive Control of Systems with Actuator Failures.- 6. Multi-mode System Identification.- 7. On Feedback Control of Processes with 'Hard' Nonlinearities.- 8. Adaptive Friction Compensation for Servo Mechanisms.- 9. Relaxed Controls and a Class of Active Material Actuator Models.- 10. Robust Adaptive Control of Nonlinear Systems with Dynamic Backlash-like Hysteresis.- 11. Adaptive Control of a Class of Time-delay Systems in the Presence of Saturation.- 12. Adaptive Control for Systems with Input Constraints - A Survey.- 13. Robust Adaptive Control of Input Rate Constrained Discrete Time Systems.- 14. Adaptive Control of Linear Systems with Poles in the Closed LHP with Constrained Inputs.- 15. Adaptive Control with Input Saturation Constraints.- 16. Adaptive Control of Linear Systems with Unknown Time Delay.
136 citations
TL;DR: This paper is concerned with establishing broadly based system theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful.
Abstract: This paper is concerned with establishing broadly based system theoretic foundations and practical techniques for the problem of system identification that are rigorous, intuitively clear and conceptually powerful. A general formulation is first given in which two order relations are postulated on a class of models: a constant one of complexity and a variable one of approximation induced by an observed behaviour. An admissible model is such that any less complex model is a worse approximation. The general problem of identification is that of finding the admissible subspace of models induced by a given behaviour. It is proved under very general assumptions that, if deterministic models are required, then nearly all behaviours require models of nearly maximum complexity. A general theory of approximation between models and behaviour is then developed based on subjective probability concepts and semantic information theory. The role of structural constraints such as causality, locality, finite memory, etc., ...
136 citations
TL;DR: In this paper, the authors model the unknown vector field using a deep neural network, imposing a Runge-Kutta integrator structure to isolate this vector field even when the data has a non-uniform timestep, thus constraining and focusing the modeling effort.
135 citations