A fuzzy neighborhood-based training algorithm for feedforward neural networks

Authors:
Mounir Ben Nasr;Mohamed Chtourou
Affiliations:
Research Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), ENIS, Department of Electrical Engineering, BP W, 3038, Sfax, Tunisia;Research Unit on Intelligent Control, Design and Optimization of Complex Systems (ICOS), ENIS, Department of Electrical Engineering, BP W, 3038, Sfax, Tunisia
Venue:
Neural Computing and Applications
Year:
2009

Citing 0
Cited 2

A constrained approximation algorithm by encoding second-order derivative information into feedforward neural networks

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Review article: A self-organizing map-based initialization for hybrid training of feedforward neural networks

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work we present a new hybrid algorithm for feedforward neural networks, which combines unsupervised and supervised learning. In this approach, we use a Kohonen algorithm with a fuzzy neighborhood for training the weights of the hidden layers and gradient descent method for training the weights of the output layer. The goal of this method is to assist the existing variable learning rate algorithms. Simulation results show the effectiveness of the proposed algorithm compared with other well-known learning methods.