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Related Experiment Videos

A hybrid neural network model for noisy data regression.

Eric W M Lee1, Chee Peng Lim, Richard K K Yuen

  • 1Department of Building and Construction, City University of Hong Kong, Hong Kong. w.m.lee@student.cityu.edu.hk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|September 21, 2004
PubMed
Summary

A new hybrid neural network, GRNNFA, combines fuzzy adaptive resonance theory (FA ART) and general regression neural network (GRNN) for faster, efficient learning. This model effectively handles noisy data and reduces computational load.

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Incremental learning systems like Fuzzy ART (FA) and General Regression Neural Network (GRNN) offer fast training.
  • Existing models may have high computational requirements for kernel information.
  • Noise in data can affect the performance of regression models.

Purpose of the Study:

  • To propose a novel hybrid neural network model, GRNNFA, by fusing FA ART and GRNN.
  • To leverage the advantages of incremental learning and reduce computational complexity.
  • To enhance noise removal capabilities and optimize kernel width for regression tasks.

Main Methods:

  • Developed a hybrid GRNNFA model integrating FA ART and GRNN.
  • Designed a clustering version of GRNN with FA for data compression and noise reduction.

Related Experiment Videos

  • Devised an adaptive gradient-based kernel width optimization algorithm with accelerated convergence.

    • Main Results:

    • The GRNNFA model retains fast training and reduces computational requirements.
    • Experiments on four benchmark datasets demonstrate GRNNFA's effectiveness compared to other approaches.
    • Successfully applied GRNNFA to predict evacuation times in Hong Kong karaoke centers, showing applicability in noisy regression.

    Conclusions:

    • The proposed GRNNFA model offers an efficient and effective solution for noisy data regression problems.
    • GRNNFA successfully integrates incremental learning, noise removal, and computational efficiency.
    • The model shows promise for real-world applications, such as emergency response time prediction.