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An incremental design of radial basis function networks.

Hao Yu, Philip D Reiner, Tiantian Xie

    IEEE Transactions on Neural Networks and Learning Systems
    |September 10, 2014
    PubMed
    Summary

    This study introduces an error correction (ErrCor) algorithm for building radial basis function (RBF) networks. ErrCor creates compact RBF networks efficiently, reducing computation time for trained models.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computational Neuroscience

    Background:

    • Radial basis function (RBF) networks are a class of artificial neural networks.
    • Training RBF networks can be computationally intensive and result in large network sizes.
    • Efficient construction and training algorithms are crucial for practical applications of RBF networks.

    Purpose of the Study:

    • To propose an offline algorithm for incrementally constructing and training radial basis function (RBF) networks.
    • To develop an algorithm that generates compact RBF networks with reduced computation times.
    • To demonstrate the robustness and efficiency of the proposed algorithm on real-world datasets and benchmark tests.

    Main Methods:

    • The error correction (ErrCor) algorithm incrementally adds RBF units in each iteration.

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  • Each added unit is designed to fit and eliminate the highest peak or lowest valley in the error surface.
  • The process continues until a predefined error level is achieved.

    • Main Results:

    • Experimental results show that ErrCor designs significantly more compact RBF networks compared to other algorithms.
    • The algorithm's robustness was confirmed through benchmark tests like the duplicate patterns test and the two-spiral problem.
    • The compactness of networks generated by ErrCor leads to substantially reduced computation times.

    Conclusions:

    • The proposed ErrCor algorithm offers an effective method for constructing compact and efficient RBF networks.
    • The incremental approach of ErrCor ensures reduced computational complexity and faster training.
    • ErrCor demonstrates strong performance and robustness, making it suitable for real-world machine learning tasks.