The Use of State Space Control Theory for Analysing Feedforward Neural Networks

  • Mirek Kárný
  • Kevin Warwick
  • Vera Kůrková
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


Multi-layer neural networks have the ability to approximate any nonlinear function [l][2][3], and are therefore used for a variety of purposes. However, in many cases, the neural network is treated as a black box, since the internal mathematics of a neural network can be hard to analyse. As the size of a neural network increases, its mathematics becomes more complex and hence harder to analyse. This chapter examines the use of concepts from state space control theory, for the analysis of feedforward neural networks. The concepts used in this chapter are observability, controllability and stability. Some can be applied completely to feedforward neural networks and others have little or no meaning in the context of neural computing. Each concept will be examined and its used for analysing feedforward neural networks discussed.


State Space Hide Layer Training Algorithm Feedforward Neural Network State Space Representation 
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 Limited 1998

Authors and Affiliations

  • Mirek Kárný
    • 1
  • Kevin Warwick
    • 2
  • Vera Kůrková
    • 3
  1. 1.Institute of Information Theory & AutomationPrague 8Czech Republic
  2. 2.Department of CyberneticsUniversity of ReadingWhiteknights, ReadingUK
  3. 3.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPrague 8Czech Republic

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