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Tensor based stacked fuzzy neural network for efficient data regression.

Jie Li1, Jiale Hu1, Guoliang Zhao1,2

  • 1College of Electronic Information Engineering, Inner Mongolia University, Hohhot, 010021 China.

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Summary
This summary is machine-generated.

This study introduces a novel stacked single fuzzy neural network using type-2 fuzzy sets and tensor structures. This hybrid approach enhances machine learning frameworks for complex data analysis.

Keywords:
Extreme learning machine (ELM)Random vector functional link network (RVFL)Tensor stacked fuzzy neural network (TSFNN)Tensor-based type-2 extreme learning machine (TT2-ELM)Tensor-based type-2 random vector functional link network (TT2-RVFL)

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Fuzzy Systems

Background:

  • Traditional Extreme Learning Machines (ELM) and Random Vector Functional Link (RVFL) networks have limitations in handling complex nonlinear data.
  • Type-2 fuzzy sets offer enhanced uncertainty handling capabilities compared to type-1 fuzzy sets.

Purpose of the Study:

  • To propose a novel hybrid fuzzy neural network architecture by integrating type-2 fuzzy sets with RVFL and ELM.
  • To introduce a tensor-based data structuring method for improved nonlinear mapping in fuzzy learning frameworks.
  • To develop a stacked single fuzzy neural network capable of learning intricate substructures.

Main Methods:

  • Extension of RVFL and ELM using type-2 fuzzy sets and vector stacking.
  • Fusion of type-2 fuzzy sets-based RVFL, type-2 fuzzy sets-based ELM, and Tikhonov-regularized ELM into a single network.
  • Tensor-based data stacking to incorporate nonlinear mappings.
  • Parameter learning for the consequent part using tensor-based matrix regression unfolding.

Main Results:

  • The proposed stacked single fuzzy neural network effectively merges three distinct algorithms into a unified tensor structure.
  • The network demonstrates the ability to learn substructures through type-2 fuzzy mappings.
  • Parameter learning via tensor-based matrix regression was successfully implemented.

Conclusions:

  • The developed stacked single fuzzy neural network presents a new paradigm for hybrid fuzzy neural network design.
  • This approach enables the use of higher-order fuzzy sets and data structures.
  • The effectiveness of the proposed network was validated through classical benchmarks and statistical testing.