Title :
Approximation capability to functions of several variables, nonlinear functionals and operators by radial basis function neural networks
Author :
Chen, Tianping ; Chen, Hong
Author_Institution :
Inst. of Math., Fudan Univ., Shanghai, China
Abstract :
The purpose of this paper is to explore the representation capability of radial basis function (RBF) neural networks. The main results are: (1) The necessary and sufficient condition for a function of one variable to be qualified as an activation function in an RBF network is that the function is not an even polynomial. (2) The capability of approximating nonlinear functionals and operators by RBF networks is revealed, using sample data either in the frequency domain or in the time domain.
Keywords :
approximation theory; feedforward neural nets; function approximation; functional equations; activation function; approximation capability; frequency domain; necessary and sufficient condition; nonlinear functionals; operators; radial basis function neural networks; time domain; Convolution; Feedforward neural networks; Intelligent networks; Kernel; Mathematics; Multi-layer neural network; Neural networks; Polynomials; Radial basis function networks; Radiofrequency interference;
Conference_Titel :
Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
Print_ISBN :
0-7803-1421-2
DOI :
10.1109/IJCNN.1993.716815
