Abstract

A hybrid method is presented to accelerate network training for traditional BP networks and to improve the classification accuracy of features for automatic visual inspection of wood veneers. In order to achieve an optimal network structure, the uniform design method is employed to optimise the parameters taking advantage of typical experimental data and good data representation, and the optimal combination is confirmed using a nonlinear quadratic programming (NLPQL) from a response surface model., and the ‘best’ level-combination is obtained to further improve the performance of the hybrid classifier. By comparison, the classifier using the optimal factors shows more powerful performance with a classification accuracy of 98.99% and a fast speed, which means greater potential for practical applications.

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