Optimizing synaptic weights of neural networks

Authors:
J. Drchal;A. Kučerová;J. Němeček
Affiliations:
Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic;Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic;Faculty of Civil Engineering, Czech Technical University in Prague, Czech Republic
Venue:
ICECT'03 Proceedings of the third international conference on Engineering computational technology
Year:
2002

Citing 3
Cited 2

Computational aspects of microplane models and a proposed parallel algorithm

Computational concrete structures technology
Fuzzy and Neural Approaches in Engineering

Fuzzy and Neural Approaches in Engineering
A competitive comparison of different types of evolutionary algorithms

ICAAICSE '01 Proceedings of the sixth international conference on Application of artificial intelligence to civil & structural engineering

Neural network weight training by mutation

Computers and Structures
2010 Special Issue: Meta-learning approach to neural network optimization

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes how the SADE genetic algorithm, we developed, could be used for training neural networks. We first make a comparison of the SADE with the traditional backpropagation method and then we demonstrate one of its applications in civil engineering.