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Methods of training and constructing multilayer perceptrons with arbitrary pattern sets

X Liang1, S Xia

  • 1Institute of Computer Science and Technology, Peking University, Beijing, P.R. China.

International Journal of Neural Systems
|September 1, 1995
PubMed
Summary
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This study introduces novel compensation methods to help multilayer perceptrons (MLPs) escape local minima during training. These techniques add neurons to guide MLPs towards global minima, improving training efficiency.

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Neural Networks

Background:

  • Multilayer perceptrons (MLPs) are challenging to train using traditional Back Propagation (BP).
  • MLPs often become trapped in local minima, hindering optimal performance.
  • Efficient training of deep learning models is a significant research area.

Purpose of the Study:

  • To present two novel compensation methods for training difficult multilayer perceptrons (MLPs).
  • To enable MLPs to escape local minima and converge to global minima.
  • To improve the training process for neural networks with complex architectures.

Main Methods:

  • Developing compensation techniques that correct erroneous outputs iteratively.
  • Adding compensatory hidden neurons for binary and real-valued input three-layer perceptrons.

Related Experiment Videos

  • Adapting compensation strategies for MLPs with more than three layers by modifying layer treatment.

    • Main Results:

    • The proposed compensation methods effectively guide MLPs out of local minima.
    • Successful correction of MLP outputs until convergence to global minima is achieved.
    • Demonstrated applicability of the methods across different MLP configurations and input types.

    Conclusions:

    • The presented compensation methods offer a viable solution for training challenging MLPs.
    • These techniques enhance the ability of MLPs to find global minima, improving model accuracy.
    • The study provides practical examples and validates the effectiveness of the proposed compensation strategies.