Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

2015
Vadim Romanuke

The research is focused on optimising two-layer perceptron for generalised scaled object classification problem. The optimisation criterion is minimisation of inaccuracy. The inaccuracy depends on training parameters and hidden layer neuron number. After its statistics is accumulated, minimisation is executed by a numerical search. Perceptron is optimised additionally by extra training. As it is done, the classification error percentage does not exceed 3% in case of the worst scale distortion.

Keywords
Extra pass training, optimisation, scaling-proof classifier, two-layer perceptron

Romanuke, V. Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem. Information Technology and Management Science. Vol.18, 2015, pp.42-48. ISSN 2255-9086. e-ISSN 2255-9094.

Publication language
English (en)