Evolutionary Artificial Neural Network Design and Training for wood veneer classification

Research output: Contribution to journalArticle

Authors

Colleges, School and Institutes

External organisations

  • Research and Enterprise Department
  • University of Wales, Newport

Abstract

This study addresses the design and the training of a Multi-Layer Perceptron classifier for identification of wood veneer defects from statistical features of wood sub-images. Previous research utilised a neural network structure manually optimised using the Taguchi method with the connection weights trained using the Backpropagation rule. The proposed approach uses the evolutionary Artificial Neural Network Generation and Training (ANNGaT) algorithm to generate the neural network system. The algorithm evolves simultaneously the neural network topology and the weights. ANNGaT optimises the size of the hidden layer(s) of the neural network structure through genetic mutations of the individuals. The number of hidden layers is a system parameter. Experimental tests show that ANNGaT produces highly compact neural network structures capable of accurate and robust learning. The tests show no differences in accuracy between neural network architectures using one and two hidden layers of processing units. Compared to the manual approach, the evolutionary algorithm generates equally performing solutions using considerably smaller architectures. Moreover, the proposed algorithm requires a lower design effort since the process is fully automated.

Details

Original languageEnglish
Pages (from-to)732-741
Number of pages10
JournalEngineering Applications of Artificial Intelligence
Volume22
Issue number4-5
Publication statusPublished - Jun 2009

Keywords

  • Artificial Neural Network Design, Artificial Neural Networks, Automated visual inspection, Evolutionary Algorithms, Pattern classification