Title :
Rates of convergence of the recursive radial basis function networks
Author :
Mazurek, J. ; Krzyzak, A. ; Cichocki, Andrzej
Author_Institution :
Neurolab. GmbH, Germany
Abstract :
Recursive radial basis function (RRBF) neural networks are introduced and discussed. We study in detail the nets with diagonal receptive field matrices. Parameters of the networks are learned by a simple procedure. Convergence and the rates of convergence of RRBF nets in the mean integrated absolute error (MIAE) sense are studied under mild conditions imposed on some of the network parameters. The obtained results also give the upper bounds on the performance of RRBF nets learned by minimizing the empirical L1 error
Keywords :
adaptive systems; approximation theory; convergence of numerical methods; error analysis; feedforward neural nets; learning (artificial intelligence); matrix algebra; network parameters; recursive functions; signal processing; adaptive learning algorithms; convergence rates; diagonal receptive field matrices; empirical L1 error minimization; function approximation; mean integrated absolute error; network parameters; neural networks; performance; processing nodes; recursive radial basis function networks; signal processing; upper bounds; Convergence; Radial basis function networks;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.595503
