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Neuromodels of Dynamic Systems

  • Dimitris C. Dracopoulos
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Abstract

Nearly all phenomena of the natural world involve systems whose behavior varies through time. In some cases the rules governing the behavior are themselves opaque, but in many cases complexity can arise from relatively simple rules. The most primitive biological information processing systems evolved to meet the necessities of survival. From sense data to action, flight or the capture of prey, there is a gap that was bridged by the evolution of adaptive control systems based on circuits of simple neural components. A vital computational characteristic of such neural circuitry is the ability to model non-linear dynamic systems.

Keywords

Lyapunov Exponent Chaotic System Dynamic System Modeling Generalization Capability Euler System 
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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Copyright information

© Springer-Verlag London 1997

Authors and Affiliations

  • Dimitris C. Dracopoulos
    • 1
  1. 1.Department of Computer ScienceBrunel UniversityUxbridge, MiddlesexUK

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