Modeling by Neural Networks

  • Igor Grabec
  • Wolfgang Sachse
Part of the Springer Series in Synergetics book series (SSSYN, volume 68)


Up to now we have considered a number of concepts needed for the development of devices capable of automatic modeling of natural phenomena from the physical description of nature. This approach has already led us to adaptive network systems which consist of a number of relatively simple information processing units. We have called these formal neurons because their adaptive properties are similar to those of biological neurons. The aim of this chapter is to explain this similarity in more detail by showing how the adaptation of biological neurons and their networks can be described by dynamical models. Because the corresponding field of research is very broad, we present only those fundamental properties which are of importance for the modeling of natural laws. For the other topics, the reader is advised to consult the literature cited in the bibliography of this chapter.


Neural Network Radial Basis Function Receptive Field Input Vector Radial Basis Function Neural Network 
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 Berlin Heidelberg 1997

Authors and Affiliations

  • Igor Grabec
    • 1
  • Wolfgang Sachse
    • 2
  1. 1.Faculty of Mechanical EngineeringUniversity of LjubljanaLjubljanaSlovenia
  2. 2.Theoretical and Applied MechanicsCornell UniversityIthacaUSA

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