Title :
Self-organization sigmoidal blocks networks
Author :
Valença, Mêuser ; Ludermir, Teresa
Author_Institution :
Companhia Hidro-Eletrica do Sao Francisco, Brazil
Abstract :
We present a new class of higher-order feedforward neural networks, called self-organization sigmoidal blocks networks (SSBN). SSBN networks are based on the inductive learning method (also called self-organization). These new networks are shown to uniformly approximate any continuous function, with arbitrary degree of accuracy. The SSBN provides a natural mechanism for incremental network growth, and we develop a constructive algorithm based on the inductive learning method for the network. Simulation results of forecasting, approximation of nonlinear functions and approximation of multivariate polynomials are given, to highlight the capability of the network
Keywords :
feedforward neural nets; forecasting theory; function approximation; learning by example; polynomial approximation; self-organising feature maps; feedforward neural networks; forecasting; higher-order neural networks; inductive learning; multivariate polynomials; nonlinear function approximation; self-organization sigmoidal blocks networks; Approximation algorithms; Data handling; Explosions; Feedforward neural networks; Function approximation; Learning systems; Mathematical model; Neural networks; Polynomials; Predictive models;
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.1999.832680
