On Structure Selection of Radial Basis Function Networks
نویسندگان
چکیده
The orthogonal least squares algorithm (OLS) and the support vector regression (SVR) are two popular approaches to choose the structure of the Radial Basis Function Network (RBFN). The former is derived based only on the modelling errors, whilst the latter also on the model complexity. A comparison of the generalization results of networks selected from the OLS and the SVR is presented here using a simulated nonlinear system, and river discharges and rainfall data of Fuji River. The RBFN based on the SVR is shown to perform better than that based on the OLS. Copyright © 2005 IFAC
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