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Related Experiment Video

Updated: May 21, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

Design optimization of wearable antennas using an adaptive network-based fuzzy inference system.

Asrafuzzaman Khan Nahin1, Waleed M Hamanah2,3, Alaa Hussein1,4

  • 1Electrical Engineering Department, College of Engineering and Physics, KFUPM, Dhahran, 31261, Saudi Arabia.

Scientific Reports
|May 19, 2026
PubMed
Summary

This study introduces a data-driven framework using Adaptive Network-Based Fuzzy Inference System (ANFIS) to optimize wearable microstrip patch antenna designs. The ANFIS model rapidly predicts antenna geometry from performance metrics, accelerating wearable device prototyping.

Keywords:
ANFISAntenna designMembership function optimizationMicrostrip patch antennaPerformance metricsWearable devices

Related Experiment Videos

Last Updated: May 21, 2026

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band
06:43

Effective Analysis of Human Exposure Conditions with Body-worn Dosimeters in the 2.4 GHz Band

Published on: May 2, 2018

Area of Science:

  • Electromagnetics
  • Antenna Engineering
  • Computational Intelligence

Background:

  • Wearable antennas require efficient design optimization for performance.
  • Traditional simulation methods can be time-consuming for iterative design.
  • Neuro-fuzzy systems offer potential for surrogate modeling in antenna design.

Purpose of the Study:

  • To develop a data-driven optimization framework for wearable microstrip patch antennas.
  • To utilize Adaptive Network-Based Fuzzy Inference System (ANFIS) for predicting antenna geometry.
  • To evaluate the impact of membership functions on ANFIS model performance.

Main Methods:

  • Generated a dataset of 500 samples using High-Frequency Structure Simulator (HFSS) simulations.
  • Trained an ANFIS model with a 70/30 training-testing split.
  • Evaluated four membership function types: triangular, trapezoidal, Gaussian, and generalized bell.

Main Results:

  • ANFIS predictions showed strong agreement with HFSS simulations.
  • Achieved sub-millimeter mean absolute errors for antenna patch dimensions.
  • The generalized bell membership function demonstrated the most stable convergence and lowest prediction error.

Conclusions:

  • The ANFIS framework enables rapid estimation of antenna geometry, reducing reliance on extensive simulations.
  • This approach facilitates faster prototyping of wearable antennas.
  • Membership function selection is critical for effective neuro-fuzzy modeling in antenna design.