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Related Experiment Videos

On-line node fault injection training algorithm for MLP networks: objective function and convergence analysis.

John Pui-Fai Sum, Chi-Sing Leung, Kevin I-J Ho

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    This study analyzes a neural network fault tolerance algorithm where nodes output zeros during training. We provide its objective function and prove convergence for multilayer perceptrons (MLPs).

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Neural Networks

    Background:

    • Fault tolerance in neural networks is a long-standing research area.
    • Existing training algorithms for fault tolerance are numerous, but theoretical analysis remains incomplete.
    • The on-line node fault injection algorithm, where hidden nodes randomly output zeros during training, lacks comprehensive theoretical understanding.

    Purpose of the Study:

    • To present the objective function for the on-line node fault injection algorithm.
    • To provide a convergence proof for this algorithm applied to multilayer perceptrons (MLPs).
    • To analyze the objective functions across different MLP output node configurations.

    Main Methods:

    • Theoretical analysis of the on-line node fault injection algorithm.
    • Derivation of objective functions for MLPs with single linear, multiple linear, and single sigmoid output nodes.
    • Convergence proof using mathematical analysis, showing convergence with probability one.

    Main Results:

    • The algorithm is shown to converge with probability one for MLPs.
    • Objective functions for MLPs with single and multiple linear output nodes share a common form, including mean square errors, a regularizer, and weight decay terms.
    • The objective function for MLPs with a single sigmoid output node differs slightly from the linear output cases.

    Conclusions:

    • The derived objective functions enable comparison of similarities and differences across various fault tolerance algorithms and MLP configurations.
    • This work contributes to a more complete theoretical understanding of node fault injection-based training algorithms.
    • The findings facilitate further research into robust neural network design and training methodologies.