A tuning strategy for microwave filter using variable universe adaptive fuzzy logic system

Peng, Sai; Cao, Weihua; Bi, Leyu; Yuan, Yan; Wu, Min

doi:10.1109/cac53003.2021.9727379

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Fuzzy logic techniques are also used in the design and analysis of microwave devices [102]. Starting from the design of microwave transistors [103], continuing in the field of microwave circuits [104] and ending with completed microwave devices, such as waveguides [105], antennas [106], power amplifiers [107], power converters [108] and others [109].…”

Section: Fuzzy Logicmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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The usage of techniques of the artificial neural networks (ANNs) in the field of microwave devices has recently increased. The advantages of ANNs in comparison with traditional full-wave methods are that the prediction speed when the traditional time-consuming iterative calculations are not required and also the complex mathematical model of the microwave device is no longer needed. Therefore, the design of microwave device could be repeated many times in real time. However, methods of artificial neural networks still lag behind traditional full-wave methods in terms of accuracy. The prediction accuracy depends on the structure of the selected neural network and also on the obtained dataset for the training of the network. Therefore, the paper presents a systematic review of the implementation of ANNs in the field of the design and analysis of microwave devices. The guidelines for the systematic literature review and the systematic mapping research procedure, as well as the Preferred Report Items for Systematic Reviews and Meta-Analysis statements (PRISMA) are used to conduct literature search and report the results. The goal of the paper is to summarize the application areas of usage of ANNs in the field of microwave devices, the type and structure of the used artificial neural networks, the type and size of the dataset, the interpolation and the augmentation of the training dataset, the training algorithm and training errors and also to discuss the future perspectives of the usage of ANNs in the field of microwave devices.

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Section: Fuzzy Logicmentioning

confidence: 99%

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

et al. 2022

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“…The epsilon decay strategy systematically finetunes this balance over time, ensuring a nuanced approach to the exploration-exploitation trade-off. Unlike rule-based systems, such as FLCs [23], the simplicity and adaptability of Q-learning reduce the need for intricate rule bases, providing a more streamlined and efficient tuning process. Moreover, the model-free nature of Q-learning contributes to its computational efficiency, a crucial factor in real-time tuning scenarios.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning Empowered Cavity Filter Tuning with Epsilon Decay Strategy

Aghanim,

Chekenbah,

Oulhaj

et al. 2024

PIER C

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In the ever-evolving landscape of engineering and technology, the optimization of complex systems is a perennial challenge. Cavity filters, pivotal in Radio Frequency (RF) systems, demand precise tuning for optimal performance. This article introduces an innovative approach to automate cavity filter tuning using Q-learning, enhanced with epsilon decay. While reinforcement learning algorithms like Q-learning have shown effectiveness in complex decision-making, the exploration-exploitation trade-off remains a crucial challenge. The study conducts a thorough investigation into the application of epsilon decay in conjunction with Q-learning, employing the well-established epsilon-greedy strategy. This research focuses on systematically decaying the exploration rate ε over time, aiming to strike a balance between exploring new actions and exploiting acquired knowledge. This strategic shift serves to not only refine the convergence of the Q-learning model but also remarkably elevate the overall tuning performances. Impressively, this optimization is achieved with a notable reduction in the number of tuning steps, demonstrating an efficiency boost of up to 45 steps.

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Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning

Sekhri,

Kapoor,

Tamre

2024

Wireless Pers Commun

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A tuning strategy for microwave filter using variable universe adaptive fuzzy logic system

Cited by 3 publications

References 8 publications

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

Trends of Microwave Devices Design Based on Artificial Neural Networks: A Review

Q-Learning Empowered Cavity Filter Tuning with Epsilon Decay Strategy

Review of State-of-the-Art Microwave Filter Tuning Techniques and Implementation of a Novel Tuning Algorithm Using Expert-Based Hybrid Learning

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