Modelling and Prediction Using GMDH Networks

  • Duc Truong Pham
  • Xing Liu


Chapters 2 and 3 have shown that neural networks can be employed to identify dynamic systems. The main advantages of neural networks over conventional identification methods include simplicity of implementation and good approximation properties [Warwick et aI, 1992]. In feedforward network based identification schemes, neural networks are used to represent the implied static mapping between the available input and output data. The network structures (number of layers and number of units in each layer) are predefined and remain unchanged both during and after training. Successful identification is often dependent on proper pre-estimation of the network structure.


Neural Network Input Unit Present Layer Good Approximation Property Conventional Identification Method 
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 1995

Authors and Affiliations

  • Duc Truong Pham
    • 1
  • Xing Liu
    • 1
  1. 1.Intelligent Systems Laboratory Systems Division, Cardiff School of EngineeringUniversity of WalesCardiffUK

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