Grey-box model identification via evolutionary computing

Tan, Kay Chen; Li, Yun

doi:10.1016/s0967-0661(02)00031-x

Cited by 50 publications

(28 citation statements)

References 26 publications

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“…Grey-box was developed in [5], where 'grey' means the combination of black box and clear box. Take (2) for instance, the model is in a clear linear form, and differences in estimation method will only alter the value of parameters but do not change the model to be black box.…”

Section: Methodolioges and Modelsmentioning

confidence: 99%

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Cash flow prediction using a grey-box model

Yang

Opong

Moutinho

et al. 2015

2015 21st International Conference on Automation and Computing (ICAC)

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Section: Methodolioges and Modelsmentioning

confidence: 99%

“…There have been previously developed prediction models to predict cash flows in accountancy and finance. This paper applies the grey-box model developed in [5] and investigates the potential dynamic and nonlinear features of the model to cash flows that have been overlooked in previous modelling procedures.…”

Section: Introductionmentioning

confidence: 99%

Cash flow prediction using a grey-box model

Yang

Opong

Moutinho

et al. 2015

2015 21st International Conference on Automation and Computing (ICAC)

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“…A number of methods have been previously proposed for symbolic regression of nonlinear systems, but were limited to producing linear models (4) or were applied to systems composed of one or a few interacting variables (8)(9)(10)(11)(12)(13)(14)(15)(16). Here we introduce a scalable approach for automated symbolic regression, made possible by three advances introduced here: partitioning, in which equations describing each variable of the system can be synthesized separately, thereby significantly reducing the search space; automated probing, which automates experimentation in addition to modeling, leading to an automated ''scientific process'' (17)(18)(19)(20); and snipping, an ''Occam's Razor'' process that automatically simplifies and restructures models as they are synthesized to increase their accuracy, to accelerate their evaluation, and to render them more parsimonious and human-comprehensible.…”

Section: Partitioning Automated Probing and Snippingmentioning

confidence: 99%

Automated reverse engineering of nonlinear dynamical systems

Bongard

Lipson²

2007

Proc. Natl. Acad. Sci. U.S.A.

688

529

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Complex nonlinear dynamics arise in many fields of science and engineering, but uncovering the underlying differential equations directly from observations poses a challenging task. The ability to symbolically model complex networked systems is key to understanding them, an open problem in many disciplines. Here we introduce for the first time a method that can automatically generate symbolic equations for a nonlinear coupled dynamical system directly from time series data. This method is applicable to any system that can be described using sets of ordinary nonlinear differential equations, and assumes that the (possibly noisy) time series of all variables are observable. Previous automated symbolic modeling approaches of coupled physical systems produced linear models or required a nonlinear model to be provided manually. The advance presented here is made possible by allowing the method to model each (possibly coupled) variable separately, intelligently perturbing and destabilizing the system to extract its less observable characteristics, and automatically simplifying the equations during modeling. We demonstrate this method on four simulated and two real systems spanning mechanics, ecology, and systems biology. Unlike numerical models, symbolic models have explanatory value, suggesting that automated ''reverse engineering'' approaches for model-free symbolic nonlinear system identification may play an increasing role in our ability to understand progressively more complex systems in the future.coevolution ͉ modeling ͉ symbolic identification M any branches of science and engineering represent systems that change over time as sets of differential equations, in which each equation describes the rate of change of a single variable as a function of the state of other variables. The structures of these equations are usually determined by hand from first principles, and in some cases regression methods (1-3) are used to identify the parameters of these equations. Nonparametric methods that assume linearity (4) or produce numerical models (5, 6) fail to reveal the full internal structure of complex systems. A key challenge, however, is to uncover the governing equations automatically merely by perturbing and then observing the system in intelligent ways, just as a scientist would do in the presence of an experimental system. Obstacles to achieving this lay in the lack of efficient methods to search the space of symbolic equations and in assuming that precollected data are supplied to the modeling process.Determining the symbolic structure of the governing dynamics of an unknown system is especially challenging when rare yet informative behavior can go unnoticed unless the system is perturbed in very specific ways. Coarse parameter sweeps or random samplings are unlikely to catch these subtleties, and so passive machine learning methods that rely on offline analysis of precollected data may be ineffective. Instead, active learning (7) processes that are able to generate new perturbations or seek out informative part...

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“…tems design Multi-objective evolutionary algorithms have been successfully applied to many problems in the field of control systems engineering, from the offline design of robust controllers for a coal-fired gasification plant (Griffin et al, 2000) to model identification of nonlinear systems (Tan and Li, 2002). Whilst the majority of the applications of evolutionary multi-objective optimisation in control systems engineering have been in offline applications due to the iterative nature of the evolutionary design process, they have also been used in online applications applications such as hardware-in-the-loop tuning of a fuzzy logic based DC motor controller (Stewart et al, 2004).…”

Section: Multi-objective Evolutionary Algorithms In Control Sys-mentioning

confidence: 99%

Multi-objective Evolutionary Design of Robust Controllers on the Grid

Shenfield

Fleming

2011

IFAC Proceedings Volumes

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Coupling conventional controller design methods, model based controller synthesis and simulation, and multi-objective evolutionary optimisation methods frequently results in an extremely computationally expensive design process. However, the emerging paradigm of grid computing provides a powerful platform for the solution of such problems by providing transparent access to large-scale distributed high-performance compute resources. As well as substantially speeding up the time taken to find a single controller design satisfying a set of performance requirements this gridenabled design process allows a designer to effectively explore the solution space of potential candidate solutions. An example of this is in the multi-objective evolutionary design of robust controllers, where each candidate controller design has to be synthesised and the resulting performance of the compensated system evaluated by computer simulation. This paper introduces a grid-enabled framework for the multi-objective optimisation of computationally expensive problems which will then be demonstrated using and example of the multi-objective evolutionary design of a robust lateral stability controller for a real-world aircraft using H ∞ loop shaping.

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Grey-box model identification via evolutionary computing

Cited by 50 publications

References 26 publications

Cash flow prediction using a grey-box model

Cash flow prediction using a grey-box model

Automated reverse engineering of nonlinear dynamical systems

Multi-objective Evolutionary Design of Robust Controllers on the Grid

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