TWRBF – Transductive RBF Neural Network with Weighted Data Normalization

  • Qun Song
  • Nikola Kasabov
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)


This paper introduces a novel RBF model – Transductive Radial Basis Function Neural Network with Weighted Data Normalization (TWRBF). In transductive systems a local model is developed for every new input vector, based on some closest to this vector data from the training data set. The Weighted Data Normalization method (WDN) optimizes the data normalization range individually for each input variable of the system. A gradient descent algorithm is used for training the TWRBF model. The TWRBF is illustrated on two case study prediction/identification problems. The first one is a prediction problem of the Mackey-Glass time series and the second one is a real medical decision support problem of estimating the level of renal functions in patients. The proposed TWRBF method not only gives a good accuracy for an individual, “personalized” model, but depicts the most significant variables for this model.


Root Mean Square Error Radial Basis Function Radial Basis Function Neural Network Radial Basis Function Network Time Series Prediction 
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 2004

Authors and Affiliations

  • Qun Song
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering & Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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