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A deep belief network with PLSR for nonlinear system modeling.

Junfei Qiao1, Gongming Wang1, Wenjing Li1

  • 1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 6, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Partial Least Square Regression-Deep Belief Network (PLSR-DBN) model for improved nonlinear system modeling. PLSR-DBN overcomes gradient-based limitations, enhancing accuracy and training efficiency in complex system identification.

Keywords:
Deep belief networkNonlinear system modelingPartial least square regressionWastewater treatment systemWeights optimization

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Area of Science:

  • Engineering
  • Machine Learning
  • Data Science

Background:

  • Deep Belief Networks (DBNs) are effective for nonlinear system modeling but suffer from local optima due to gradient-based weight optimization.
  • Existing methods often yield suboptimal training results, limiting their practical application in complex engineering scenarios.

Purpose of the Study:

  • To propose a novel Deep Belief Network (DBN) model optimized with Partial Least Square Regression (PLSR) for enhanced nonlinear system modeling and identification.
  • To address the limitations of gradient-based optimization in DBNs by introducing a layer-by-layer PLSR approach for weight optimization.

Main Methods:

  • Utilized unsupervised contrastive divergence (CD) algorithm for initial DBN weight initialization.
  • Implemented layer-by-layer Partial Least Square Regression (PLSR) to optimize weights from the top layer downwards, replacing traditional gradient methods.
  • Provided theoretical analysis of convergence to ensure the model's effectiveness.

Main Results:

  • The proposed PLSR-DBN demonstrated superior performance in terms of accuracy and speed compared to existing methods.
  • Achieved high-dimensional nonlinear mapping and modeling capabilities, validated on benchmark systems and real-world wastewater treatment data.
  • Successfully applied to handwritten digit recognition, showcasing its versatility.

Conclusions:

  • The PLSR-DBN model offers a significant advancement in nonlinear system modeling by overcoming local optima issues inherent in gradient-based DBN training.
  • The layer-by-layer PLSR optimization provides a robust and efficient alternative, leading to improved accuracy and faster convergence.
  • The model's effectiveness is confirmed across diverse applications, including engineering systems and pattern recognition.