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

  • Chengdong Li
  • Weina Ren
  • Jianqiang Yi
  • Guiqing Zhang
  • Fang Shang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

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.

Keywords

data-driven optimization single input rule module least square method fuzzy system cooling and heating loads 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  • Chengdong Li
    • 1
  • Weina Ren
    • 1
  • Jianqiang Yi
    • 2
  • Guiqing Zhang
    • 1
  • Fang Shang
    • 1
  1. 1.School of Information and Electrical EngineeringShandong Jianzhu UniversityJinanChina
  2. 2.Institute of AutomationChinese Academy of SciencesBeijingChina

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