Title :
Using separable functional network for function approximation
Author :
Zhou, Yongquan ; Liu, Bai ; Huang, Huajuan ; Wei, Xingqong
Author_Institution :
Coll. of Math. & Comput. Sci., Guangxi Univ. for Nat., Nanning
Abstract :
In this paper, separable functional network architecture and a learning algorithm of separable functional network are proposed, the learning of functional parameters use Lagrange multipliers by means of auxiliary function and solving a system of linear equations obtain parameters. An experiment in approximating typical continuous functions is given. The results show that the learning algorithm presented in the paper has excellent performance in approximation error.
Keywords :
function approximation; learning (artificial intelligence); mathematics computing; multiplying circuits; Lagrange multiplier; auxiliary function; function approximation; learning algorithm; linear equation; separable functional network; Approximation algorithms; Computer architecture; Computer science; Educational institutions; Equations; Function approximation; Lagrangian functions; Mathematics; Network topology; Neurons; function approximation; functional network; functional parameters; learning algorithm; separable functional network;
Conference_Titel :
Granular Computing, 2008. GrC 2008. IEEE International Conference on
Conference_Location :
Hangzhou
Print_ISBN :
978-1-4244-2512-9
Electronic_ISBN :
978-1-4244-2513-6
DOI :
10.1109/GRC.2008.4664636
