Modelling Complex Systems

  • Alistair I. Mees
Part of the Mathematical Modelling book series (MMO, volume 6)


Traditionally, mathematical models of biological systems have mostly been phenomenological because of the difficulty of deriving a set of equations from first principles in the way that can typically be done in the physical sciences. Sometimes, attempts are made to fit a parametrised model to data, but the fit is usually very bad and all that is hoped for is a qualitative understanding. Recent work in dynamical systems theory has made it possible to construct non-parametric models directly from data. This paper describes a particular approach—embedding followed by tesselation—which appears able to capture the main features of some biological systems, and to provide a degree of quantitative predictive power.


Strange Attractor Dimensional Manifold Multivariate Adaptive Regression Spline Dynamical System Theory Original Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Birkhäuser Boston 1990

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  • Alistair I. Mees

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