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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Stochastic and deterministic processes in Asymmetric Tsetlin Machine.

Negar Elmisadr1, Mohamed-Bachir Belaid2, Anis Yazidi1

  • 1Department of Computer Science, Faculty of Technology, Art and Design, OsloMet-Oslo Metropolitan University, Oslo, Norway.

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Summary
This summary is machine-generated.

This study enhances the Tsetlin Machine (TM) using stochasticity and asymmetry. The new Asymmetric Tsetlin (AT) machine shows superior performance on complex datasets, improving decision-making capabilities.

Keywords:
Asymmetric Probabilistic Tsetlin (APT) MachineAsymmetric Tsetlin (AT) MachineStochastic Point Location (SPL) algorithmTsetlin Machine (TM)cumulative distribution function (CDF)decaying normal distribution functionprobabilistic and deterministic behavior

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • The Tsetlin Machine (TM) is a powerful pattern recognition model.
  • Enhancing TM adaptability and decision-making is crucial for complex tasks.
  • Existing TM variants lack sufficient adaptability and stochasticity.

Purpose of the Study:

  • To introduce novel enhancements to the Tsetlin Machine (TM) for improved decision-making.
  • To integrate stochasticity and asymmetry into the TM framework.
  • To develop and evaluate new TM variants, namely the Asymmetric Probabilistic Tsetlin (APT) Machine and the Asymmetric Tsetlin (AT) Machine.

Main Methods:

  • Incorporation of the Stochastic Point Location (SPL) algorithm.
  • Implementation of the Asymmetric Steps technique.
  • Utilizing a decaying normal distribution function for adaptive learning.

Main Results:

  • The Asymmetric Tsetlin (AT) and Asymmetric Probabilistic Tsetlin (APT) machines were developed.
  • Both AT and APT models demonstrated competitive performance against traditional algorithms and classical Tsetlin machines.
  • The AT model exhibited superior performance, particularly on complex benchmark datasets.

Conclusions:

  • The proposed enhancements significantly improve TM decision-making capabilities.
  • The AT machine offers a robust and adaptable solution for complex pattern recognition tasks.
  • This research paves the way for more sophisticated and efficient Tsetlin Machine applications.