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Deep Cascade Gradient RBF Networks With Output-Relevant Feature Extraction and Adaptation for Nonlinear and

Tong Liu, Zeyue Tian, Sheng Chen

IEEE Transactions on Cybernetics

|March 3, 2022

Summary

This summary is machine-generated.

This study introduces a novel deep cascade gradient radial basis function (GRBF) network for industrial predictive modeling. The method effectively handles big data from nonstationary processes, improving online prediction accuracy and adapting to dynamic changes.

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Deep Neural Networks for Image-Based Dietary Assessment

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Published on: March 13, 2021

Area of Science:

Industrial Process Modeling
Machine Learning
Data Science

Background:

Industrial predictive models face challenges with big data from high-dimensional, nonstationary processes.
Deep networks like stacked autoencoders (SAE) extract features but struggle with online adaptation to fast dynamics.

Purpose of the Study:

To propose a deep cascade gradient radial basis function (GRBF) network for online modeling and prediction of nonlinear and nonstationary industrial processes.
To integrate deep feature learning with online adaptation capabilities.

Main Methods:

A deep cascade GRBF network combining a GRBF weak predictor and a stacked autoencoder (SAE) for feature extraction.
Online updating of the GRBF predictor's weights and structure to capture time-varying process characteristics.

Main Results:

The proposed deep cascade GRBF network demonstrated superior performance compared to existing online modeling approaches and deep networks.
Achieved higher online prediction accuracy and improved computational efficiency in real-world industrial case studies.

Conclusions:

The deep cascade GRBF network offers an effective solution for online modeling of complex industrial processes.
The method successfully integrates feature learning and online adaptation for improved predictive accuracy and dynamic tracking.