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

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  • Romanuke Vadim

    (Khmelnitsky National University)

Abstract

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.

Suggested Citation

  • Romanuke Vadim, 2015. "Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem," Information Technology and Management Science, Sciendo, vol. 18(1), pages 42-48, December.
    • Handle: RePEc:vrs:itmasc:v:18:y:2015:i:1:p:42-48:n:7
    DOI: 10.1515/itms-2015-0007
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    1. Chen, Zhixiang & Cao, Feilong & Hu, Jinjie, 2015. "Approximation by network operators with logistic activation functions," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 565-571.
    2. Püschel, Tim & Schryen, Guido & Hristova, Diana & Neumann, Dirk, 2015. "Revenue management for Cloud computing providers: Decision models for service admission control under non-probabilistic uncertainty," European Journal of Operational Research, Elsevier, vol. 244(2), pages 637-647.
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