Robust multilayer neural network based on median neuron model


ALADAĞ Ç. H. , EĞRİOĞLU E., YOLCU U.

NEURAL COMPUTING & APPLICATIONS, cilt.24, ss.945-956, 2014 (SCI İndekslerine Giren Dergi) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 24
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1007/s00521-012-1315-5
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Sayfa Sayıları: ss.945-956

Özet

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.