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Related Experiment Video

Updated: Mar 14, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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An Incremental Radial Basis Function Network Based on Information Granules and Its Application.

Myung-Won Lee1, Keun-Chang Kwak1

  • 1Department of Control and Instrumentation Engineering, Chosun University, 375 Seosuk-dong, Dong-gu, Gwangju 501-759, Republic of Korea.

Computational Intelligence and Neuroscience
|October 5, 2016
PubMed
Summary
This summary is machine-generated.

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This study introduces an Incremental Radial Basis Function Network (IRBFN) for predicting residential heating and cooling loads. The new model improves energy performance prediction accuracy compared to existing methods.

Area of Science:

  • Building energy modeling
  • Artificial intelligence in civil engineering
  • Computational intelligence for energy systems

Background:

  • Accurate prediction of heating and cooling loads is crucial for residential building energy efficiency.
  • Existing models like Linear Regression (LR) and standard Radial Basis Function Networks (RBFN) have limitations in capturing complex nonlinearities.
  • Integrating global and local models can enhance prediction accuracy.

Purpose of the Study:

  • To design an Incremental Radial Basis Function Network (IRBFN) for improved heating and cooling load prediction in residential buildings.
  • To combine Linear Regression (LR) for global modeling with local RBFN for capturing nonlinearities.
  • To utilize Context-based Fuzzy C-Means (CFCM) clustering for creating information granules guided by LR model errors.

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Main Methods:

  • Development of an Incremental Radial Basis Function Network (IRBFN).
  • Integration of Linear Regression (LR) as a global model, refined by a local RBFN.
  • Application of Context-based Fuzzy C-Means (CFCM) clustering for information granule generation.
  • Experimental validation on energy performance data of 768 diverse residential buildings.

Main Results:

  • The proposed IRBFN demonstrated superior performance in predicting heating and cooling loads.
  • IRBFN outperformed Linear Regression (LR), standard RBFN, RBFN with information granules, and Linguistic Model (LM).
  • The model effectively captured localized nonlinearities missed by the global LR model.

Conclusions:

  • The IRBFN offers a robust and accurate approach for estimating residential building energy performance.
  • Combining global linear models with local fuzzy-based RBFN significantly enhances prediction accuracy.
  • The proposed method provides a valuable tool for optimizing energy management in buildings.