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Computer State Evaluation Using Adaptive Neuro-Fuzzy Inference Systems.

Abror Buriboev1, Azamjon Muminov2

  • 1Department of IT, Samarkand Branch of Tashkent University of Information Technologies, Samarkand 140100, Uzbekistan.

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Summary

This study introduces a simplified method using adaptive neuro-fuzzy inference system (ANFIS) models to analyze computer component impact on CPU performance. The approach simplifies complex metrics for better system state assessment and bottleneck identification.

Keywords:
CPU utilizationMamdani and Sugeno adaptive neuro-fuzzy inference systemcomplex evaluation

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • System Performance Analysis

Background:

  • System design relies on predictive resource consumption analysis.
  • Assessing computer state requires analyzing component utilizations and workloads.
  • Existing evaluation techniques use complex metrics, hindering practical application.

Purpose of the Study:

  • To examine the impact of memory, cache, storage, and bus utilization on CPU performance.
  • To develop a simplified evaluation method using linguistic values of component utilization.
  • To determine computer system states using adaptive neuro-fuzzy inference system (ANFIS) models.

Main Methods:

  • Utilizing adaptive neuro-fuzzy inference system (ANFIS) models (Sugeno and Mamdani types).
  • Applying fuzzy set theory for impact analysis.
  • Monitoring computer component behavior and utilization percentages.

Main Results:

  • Proposed a simplified evaluation method based on linguistic values of component utilization.
  • ANFIS models provide both linguistic and quantitative results for performance analysis.
  • Demonstrated the ability to identify relationships between components and their impact on CPU performance.

Conclusions:

  • The developed method offers a user-friendly approach to computer system performance evaluation.
  • The ANFIS-based method can be applied to various computing systems, from personal computers to supercomputers.
  • The approach simplifies bottleneck identification and understanding component interdependencies.