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A Radial Basis Function network training algorithm using a non-symmetric partition of the input space - Application to a Model Predictive Control configuration
Authors:Alex Alexandridis  Haralambos SarimveisKonstantinos Ninos
Affiliation:a Department of Electronics, Technological Educational Institute of Athens, Agiou Spiridonos, Aigaleo 12210, Greece
b School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Street, Zografou Campus, Athens 15780, Greece
Abstract:This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times.
Keywords:Neural networks  Radial basis function  Fuzzy means  Fuzzy partition  Non-symmetric partition  Model predictive control  Mackey-Glass  Continuous stirred tank reactor
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