A deterministic model selection scheme for incremental RBFNN construction in time series forecasting

Abstract

This paper presents a fast and new deterministic model selection methodology for incremental radial basis function neural network (RBFNN) construction in time series prediction problems. The development of such special designed methodology is motivated by the problems that arise when using a K-fold cross-validation-based model selection methodology for this paradigm: its random nature and the subjective decision for a proper value of K, resulting in large bias for low values and high variance and computational cost for high values. Taking into account these drawbacks, the proposed model selection approach is a combined algorithm that takes advantage of two balanced and representative training and validation sets for their use in RBFNN initialization, optimization and network model evaluation. This way, the model prediction accuracy is improved, getting small variance and bias, reducing the computation time spent in selecting the model and avoiding random and computationally expensive model selection methodologies based on K-fold cross-validation procedures.

Particle swarm optimization-based radial basis function network for estimation of reference evapotranspiration

“…Radial basis function network (RBFN) (Broomhead and Lowe 1988) is an ANN technique with three layers: input layer, hidden layer, and output layer. RBFNs have been applied to solve problems in different application fields such as engineering (Javan et al 2013;Xiong et al 2013;Yin et al 2013), medical diagnosis (Jenifer et al 2014;Mateo and Rieta 2013), planning and control of logistics activities (Mehrsai et al 2013), mathematics (Kumar and Yadav 2011), computer science (Harpham and Dawson 2006;Fernández-Navarro et al 2011;Wang et al 2012), and meteorological and hydrological studies (Kagoda et al 2010;Tabari et al 2010;Marofi et al 2011;Florido et al 2012). Furthermore, Ladlani et al (2012) applied RBF to model daily ET 0 in the north of Algeria.…”