Evolutionary Artificial Neural Network Design and Training for wood veneer classification

Marco Castellani*, Hefin Rowlands

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review


    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. (C) 2009 Elsevier Ltd. All rights reserved.

    Original languageEnglish
    Pages (from-to)732-741
    Number of pages10
    JournalEngineering applications of artificial intelligence
    Issue number4-5
    Publication statusPublished - Jun 2009


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


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