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Application of Integrated Neural Network and Nature-Inspired Approach to Demand Prediction

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Intelligent Information and Database Systems (ACIIDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9011))

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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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Author information

Authors and Affiliations

  1. Department of Business Administration, DE LIN Institute of Technology, No. 1, Ln 380, Qingyun Rd., New Taipei City, 23654, Taiwan

    Zhen-Yao Chen

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  1. Zhen-Yao Chen
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Correspondence to Zhen-Yao Chen .

Editor information

Editors and Affiliations

  1. Ton Duc Thang University, Ho Chi Minh city, Vietnam

    Ngoc Thanh Nguyen

  2. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

  3. Bina Nusantara University, Jakarta, Indonesia

    Raymond Kosala

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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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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-15701-6

  • Online ISBN: 978-3-319-15702-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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