New Multilayer Neural Networks With NO Estimator and Winsorized Mean

Özdemir A. F., Dilber B.

12th INTERNATIONAL STATISTICS DAYS CONFERENCE, İzmir, Turkey, 13 - 16 October 2022, pp.42

  • Publication Type: Conference Paper / Summary Text
  • City: İzmir
  • Country: Turkey
  • Page Numbers: pp.42
  • Dokuz Eylül University Affiliated: Yes


Multilayer feed forward neural networks have been widely used for prediction, forecasting and classification over the past few years. However, it is a known fact that the mostly preferred Mc - Culloch Pitts neuron model used in these network types does not give a successful prediction performance in data sets with outliers. Therefore, robust neuron models using median and trimmed mean aggregation functions have been proposed. However, these studies were generally focused on time series forecasting. In this study, we developed new neuron models using NO estimator and the Winsorized mean for prediction, classification, and time series forecasting. NO is a quantile estimator with weights determined by using a subsampling approach. For estimating a population quantile, it uses all order statistics in a sample and the accompanying weights of the order statistics are calculated from a Binomial Distribution. The proposed NO and Winsorized mean neuron models are not sensitive to outlying observations. Back propagation, particle swarm optimization and artificial bee colony optimization algorithms were used when training multilayer neural networks and several activation functions such as sigmoid, hyperbolic, tangent, and rectified linear unit were tried. All steps of the study were performed using statistical programming language R. The written functions of the proposed neural networks enable the prediction of new observations and observing the change of errors at each iteration by providing a dynamic plot. The developed methods were applied on the real data sets and their performances were compared with the existing ones. More successful results were achieved in terms of different performance criterions