A new multilayer feedforward network based on trimmed mean neuron model

Yolcu, Ufuk and Bas, Eren and Egrioglu, Erol and Aladag, Cagdas Hakan (2015) A new multilayer feedforward network based on trimmed mean neuron model. Neural Network World, 25 (6). pp. 587-602. ISSN 1210-0552

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Abstract

The multilayer perceptron model has been suggested as an alternative to conventional approaches, and can accurately forecast time series. Additionally, several novel artificial neural network models have been proposed as alternatives to the multilayer perceptron model, which have used (for example) the generalized- mean, geometric mean, and multiplicative neuron models. Although all of these artificial neural network models can produce successful forecasts, their aggregation functions mean that they are negatively affected by outliers. In this study, we propose a new multilayer, feed forward neural network model, which is a robust model that uses the trimmed mean neuron model. Its aggregation function does not depend on outliers. We trained this multilayer, feed forward neural network using modified particle swarm optimization. We applied the proposed method to three well-known time series, and our results suggest that it produces superior forecasts when compared with similar methods.

Item Type:
Journal Article
Journal or Publication Title:
Neural Network World
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1702
Subjects:
ID Code:
139464
Deposited By:
Deposited On:
09 Dec 2019 11:50
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Aug 2020 05:54