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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Broad learning system based on fractional order optimization.

Dan Zhang1, Tong Zhang2, Zhang Tao3

  • 1College of Mechanical and Electrical Engineering, Dalian Minzu University, Dalian, 116600, China.

Neural Networks : the Official Journal of the International Neural Network Society
|April 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces fractional order optimization into the broad learning system (BLS), enhancing its data processing capabilities. The novel FOBLS method improves incremental learning and outlier handling by incorporating long-term memory characteristics.

Keywords:
Broad learning systemDynamic incremental learningFractional derivativeFractional order optimization

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Deep Learning

Background:

  • The broad learning system (BLS) is recognized for efficient incremental learning.
  • Maximum correntropy criterion (MCC) enhances BLS performance with outliers.
  • Differential equations model deep learning propagation, suggesting applicability to BLS optimization.

Purpose of the Study:

  • To introduce fractional order optimization into BLS for improved data processing.
  • To leverage the long-term memory property of fractional differential equations.
  • To enhance the weight optimization and network optimization of BLS.

Main Methods:

  • Developed a fractional order broad learning system (FOBLS).
  • Incorporated fractional order optimization into the BLS weight optimization process.
  • Constructed a dynamic incremental learning system based on fractional order.

Main Results:

  • The proposed FOBLS method demonstrates excellent performance.
  • Fractional order optimization enhances BLS data processing capabilities.
  • Long-term memory characteristics are effectively integrated into the learning process.

Conclusions:

  • Fractional order optimization is a promising approach for enhancing BLS.
  • FOBLS offers superior performance in incremental learning and outlier handling.
  • The integration of fractional calculus advances the capabilities of broad learning systems.