Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems

Mengqing Sun

doi:10.47297/taposatWSP2633-456913.20250612

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Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems

6卷第12期

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Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems
Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems
University of Shanghai for Science and Technology, Shanghai, 200093, China
2025年6卷第12期页码：61-66
纸质出版：2025-12-30
DOI：10.47297/taposatWSP2633-456913.20250612
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Mengqing Sun. (2025). Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems. 科技理论与实践（英文版）, 6(12), 61-66.

Sun Mengqing.Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems[J].Theory and Practice of Science and Technology,2025,06(12):61-66.
Mengqing Sun. (2025). Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems. 科技理论与实践（英文版）, 6(12), 61-66. DOI： 10.47297/taposatWSP2633-456913.20250612.

Sun Mengqing.Intelligent Dynamic Modeling and Adaptive Optimization for Non-Invasive Microwave Hyperthermia Systems[J].Theory and Practice of Science and Technology,2025,06(12):61-66. DOI： 10.47297/taposatWSP2633-456913.20250612.

摘要

Abstract

Microwave hyperthermia is an emerging non-invasive therapeutic modality for cancer treatment that relies on controlled heating of tumor tissue. Accurate prediction of internal temperature is critical to ensure therapeutic efficacy while avoiding damage to surrounding healthy tissues. This paper proposes a hybrid system identification framework that integrates the Sooty Tern Optimization Algorithm (STOA) with a Forgetting Factor Recursive Least Squares (FFRLS) algorithm to estimate the internal temperature of microwave-heated tissues based on surface temperature measurements. A linear-in-parameters model is developed to capture the dynamic thermal behavior of tissue heating and heat conduction. The STOA is employed to perform global exploration of the parameter space and effectively mitigate premature convergence to local optima

while the FFRLS algorithm provides online refinement and real-time adaptive updating of model parameters. Experimental results demonstrate that the proposed STOA-FFRLS method significantly outperforms the conventional RLS approach in terms of estimation accuracy and numerical stability

yielding substantially reduced modeling errors and residual fluctuations. The validated model facilitates the development of a non-invasive

high-precision thermal monitoring framework for safe and effective microwave hyperthermia treatment.

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references

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