Multiplicative neuron model artificial neural network based on Gaussian activation function. Issue 4 (May 2016)

• Record Type: Journal Article
• Authors: Gundogdu, Ozge; Egrioglu, Erol; Aladag, Cagdas; Yolcu, Ufuk
• Is Part Of: Neural computing & applications. Volume 27:Issue 4(2016)
• Page Start: 927
• Page End: 935
• Publication Date: 2016-05
• Subjects: Artificial neural network -- Multiplicative neuron model -- Gaussian activation function -- Forecasting -- Particle swarm optimization; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1908-x
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.