Radial Basis Function Networks and Nonlinear Data Modelling
نویسنده
چکیده
Seven different Radial Basis Functions have been applied in a Feedforward Neural Network and tested on five different real or simulated multivariate modelling problems. A short theory of Radial Basis Functions is presented as well as the particular implementation of the Radial Basis Function Network (RBFN). The real world data modelling problems are; identifying the dynamic actuator characteristics of a hydraulic industrial robot, modelling carbon consumption in a metallurgic industrial process and estimation of the water content in fish food products based on NIRspectroscopy. In addition the RBFNs have been applied for modelling data generated from a simulated chemical reactor and to identify a 10-dimensional test function. Key-words : Artificial Neural Networks, Radial Basis Functions, Nonlinear data modelling, Applications. This paper has been published in Proceedings of Neuro-Nimes’92, Neural Networks and their Applications, EC2, France, 1992, pp.623-633.
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