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An NN-based SRD decomposition algorithm and its application in nonlinear compensation.

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This study introduces a neural network algorithm for sensor data nonlinearity compensation. It optimizes data decomposition, reducing computational complexity and memory usage for improved sensor calibration.

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Area of Science:

  • Sensor technology
  • Artificial intelligence
  • Data processing

Background:

  • Sensor data often exhibits nonlinearity, requiring compensation for accurate measurements.
  • Existing decomposition methods can be computationally intensive and memory-demanding.
  • Neural networks offer potential for efficient nonlinear data processing.

Purpose of the Study:

  • To develop and evaluate a neural network-based square root of descending (SRD) order decomposition algorithm.
  • To minimize computational complexity and memory space during the training process.
  • To improve the accuracy of sensor data by compensating for nonlinearity.

Main Methods:

  • Implementation of a linear decomposition algorithm with automatic optimal decomposition (N) determination.
  • Application of the algorithm to nonlinear data from an encoder.
  • Theoretical estimation of hidden nodes and analysis of decomposition precision.
  • Numerical experiments to assess algorithm performance.
  • Development of a device for angular sensor calibration.

Main Results:

  • The algorithm effectively compensates for nonlinear sensor data.
  • Reduced training time (1/√N) and memory cost (1/N) were achieved.
  • The study provides insights into estimating hidden nodes and precision variations.
  • Experimental validation using an encoder demonstrated the algorithm's efficacy.

Conclusions:

  • The proposed neural network-based SRD decomposition algorithm offers an efficient solution for nonlinear sensor data compensation.
  • The method significantly reduces computational and memory requirements.
  • The algorithm shows promise for practical applications in sensor calibration and data processing.