Normalization

Abstract. 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 normaliza-tion range individually for each input variable of the system. A gradient de-scent algorithm is used for training the TWRBF model. The TWRBF is illus-trated 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 func-tions in patients. The proposed TWRBF method not only gives a good accu-racy for an individual, “personalized ” model, but depicts the most significant variables for this model