Abstract
This study intends to enhance the learning of radial basis function network (RBFN) for function approximation using self-organizing map network (SOMN) with artificial immune system (AIS)-based algorithm (AIA) and genetic algorithm (GA) methods (i.e., IG approach). The proposed combined of SOMN with IG approach (called: SIG) algorithm integrates the auto-clustering ability of SOMN and nature-inspired approach. The simulation results revealed that SOMN, AIA and GA methods can be integrated ingeniously and proposed a hybrid SIG algorithm which aims for obtaining a more accurate learning performance. Next, method evaluation results for two benchmark problems and demand prediction exercise showed that the SIG algorithm outperforms other algorithms and the Box-Jenkins models in accuracy.
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Chen, ZY. (2015). Application of Integrated Neural Network and Nature-Inspired Approach to Demand Prediction. In: Nguyen, N., Trawiński, B., Kosala, R. (eds) Intelligent Information and Database Systems. ACIIDS 2015. Lecture Notes in Computer Science(), vol 9011. Springer, Cham. https://doi.org/10.1007/978-3-319-15702-3_34
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DOI: https://doi.org/10.1007/978-3-319-15702-3_34
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