Title :
Approximation to continuous functionals and operators using adaptive higher-order feedforward neural networks
Author :
Xu, Skuxiang ; Zhang, Ming
Author_Institution :
Dept. of Comput. & Inf. Syst., Univ. of Western Sydney, Campbelltown, NSW, Australia
Abstract :
The approximation capabilities of adaptive higher-order feedforward neural network (AHFNN) with neuron-adaptive activation function (NAF) to any nonlinear continuous functional and any nonlinear continuous operator are studied. Universal approximation theorems of AHFNN to continuous functionals and continuous operators are given, and learning algorithms based on the steepest descent rule are derived to tune the free parameters in NAF as well as connection weights between neurons. We apply the algorithms to approximate continuous dynamical systems
Keywords :
adaptive systems; feedforward neural nets; function approximation; learning (artificial intelligence); connection weights; continuous dynamical systems; feedforward neural networks; functional approximation; learning algorithms; neuron-adaptive activation function; steepest descent rule; Approximation algorithms; Australia; Computer networks; Design engineering; Feedforward neural networks; Function approximation; Information systems; Neural networks; Neurons; Pattern recognition;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831521
