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Data-Driven Optimization of SIRMs Connected Neural-Fuzzy System with Application to Cooling and Heating Loads Prediction

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Advances in Neural Networks – ISNN 2015 (ISNN 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

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Abstract

In modeling, prediction and control applications, the single-input-rule-modules (SIRMs) connected fuzzy inference method can efficiently tackle the rule explosion problem that conventional fuzzy systems always face. In this paper, to improve the learning performance of the SIRMs method, a neural structure is presented. Then, based on the least square method, a novel parameter learning algorithm is proposed for the optimization of the SIRMs connected neural-fuzzy system. Further, the proposed neural-fuzzy system is applied to the cooling and heating loads prediction which is a popular multi-variable problem in the research domain of intelligent buildings. Simulation and comparison results are also given to demonstrate the effectiveness and superiority of the proposed method.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, China

    Chengdong Li, Weina Ren, Guiqing Zhang & Fang Shang

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jianqiang Yi

Authors
  1. Chengdong Li
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  2. Weina Ren
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  3. Jianqiang Yi
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  4. Guiqing Zhang
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  5. Fang Shang
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Corresponding author

Correspondence to Chengdong Li .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Li, C., Ren, W., Yi, J., Zhang, G., Shang, F. (2015). Data-Driven Optimization of SIRMs Connected Neural-Fuzzy System with Application to Cooling and Heating Loads Prediction. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_55

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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