Dynamic node creation and fast learning algorithm for a hybrid feedforward neural network

Presents an algorithm of dynamic node creation and weights learning for a hybrid feedforward neural network (HFNN) which consists of a linear model and a multilayer neural network. The algorithm is only based on linear least squares, and no iterative learning process is needed. According to the dema...

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Bibliographic Details
Published in:International Conference on Machine Learning and Cybernetics 2002 Vol. 1; pp. 202 - 205 vol.1
Main Authors: Hong-Fei Xia, Lian-Kui Dai
Format: Conference Proceeding
Language:English
Published: IEEE 2002
Subjects:
Convergence
Feedforward neural networks
Heuristic algorithms
Iterative algorithms
Laboratories
Least squares methods
Multi-layer neural network
Neural networks
Signal processing algorithms
Stochastic processes
ISBN:9780780375086, 0780375084
Online Access:Get full text
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Summary:Presents an algorithm of dynamic node creation and weights learning for a hybrid feedforward neural network (HFNN) which consists of a linear model and a multilayer neural network. The algorithm is only based on linear least squares, and no iterative learning process is needed. According to the demand of model precision, the algorithm determines the best weights of network and the minimal number of hidden nodes automatically. Compared with the well-known backpropagation network, simulation results show that the new learning algorithm for the HFNN is efficient in model precision, rate of convergence and generalization ability.
ISBN:9780780375086
0780375084
DOI:10.1109/ICMLC.2002.1176739