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Semi-tensor product-based fuzzy relation matrix technique for gear system state forecasting.

Hong L Lyu1, Wilson Wang2, Xiao P Liu3

  • 1Department of Computer Science, Lakehead University, Thunder Bay, ON, Canada.

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Summary
This summary is machine-generated.

This study introduces a hierarchical fuzzy state modeling technique to simplify complex fuzzy prediction systems. This method reduces fuzzy relation matrix dimensions for more accurate system state and gear health forecasting.

Keywords:
Fuzzy prediction modelFuzzy relation matrixGear system heath state forecastingHierarchical structure

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Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Modeling multiple-variable fuzzy prediction systems presents challenges due to complex fuzzy reasoning.
  • High-dimensional fuzzy relation matrices (FRM) complicate system state forecasting.

Purpose of the Study:

  • To propose a hierarchical fuzzy state modeling technique for simplifying complex fuzzy systems.
  • To reduce the dimensionality of fuzzy relation matrices for improved system state forecasting.

Main Methods:

  • Decomposition of high-dimensional FRM into lower-dimensional models.
  • Application of the semi-tensor product to develop a fuzzy logic framework.
  • Reduction of fuzzy rules within the fuzzy logic framework.

Main Results:

  • Successfully implemented a hierarchical fuzzy model for gear system health state forecasting.
  • Trained system parameters to enhance fuzzy reasoning accuracy.
  • Verified the effectiveness of the hierarchical FRM modeling and system identification through experimental tests.

Conclusions:

  • The proposed hierarchical fuzzy state modeling technique effectively simplifies complex fuzzy systems.
  • The method enhances accuracy in system state and gear health forecasting.
  • Experimental validation confirms the robustness of the developed techniques.