Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

Zirak, Ali Reza; Roshani, Sobhan

doi:10.14569/ijacsa.2016.070629

Cited by 2 publications

(4 citation statements)

References 18 publications

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“…In this section, a comparison between the performance of the proposed algorithm and the other researches has been performed, which is listed in Table 9. As can be seen in Table 9, the ANN method is used in [37] for modeling of the amplifier and predict output parameter of the amplifier. Additionally, the ANN method is used in [43] for predicting load resistance and mutual inductance parameters in the WPT system.…”

Section: Results Comparisonmentioning

confidence: 99%

“…Recently, ANN techniques have been used to solve several engineering and electronics problems [33][34][35][36]. ANN have also been used to model microwave circuits behavior [37][38][39][40]. A class-F amplifier at 1.8 GHz has been designed and modeled in [37], where the radial basis function (RBF) type of neural network has been utilized to model the amplifier circuit.…”

Section: Power Supplymentioning

confidence: 99%

“…ANN have also been used to model microwave circuits behavior [37][38][39][40]. A class-F amplifier at 1.8 GHz has been designed and modeled in [37], where the radial basis function (RBF) type of neural network has been utilized to model the amplifier circuit. A PSO algorithm is used in [41] to improve the efficiency and frequency of the WPT system.…”

Section: Power Supplymentioning

confidence: 99%

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A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications

et al. 2022

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This paper presents a new approach to simplify the design of class-E power amplifier (PA) using hybrid artificial neural-optimization network modeling. The class-E PA is designed for wireless power transfer (WPT) applications to be used in biomedical or internet of things (IoT) devices. Artificial neural network (ANN) models are combined with optimization algorithms to support the design of the class-E PA. In several amplifier circuits, the closed form equations cannot be extracted. Hence, the complicated numerical calculations are needed to find the circuit elements values and then to design the amplifier. Therefore, for the first time, ANN modeling is proposed in this paper to predict the values of the circuit elements without using the complex equations. In comparison with the other similar models, high accuracy has been obtained for the proposed model with mean absolute errors (MAEs) of 0.0110 and 0.0099, for train and test results. Moreover, root mean square errors (RMSEs) of 0.0163 and 0.0124 have been achieved for train and test results for the proposed model. Moreover, the best and the worst-case related errors of 0.001 and 0.168 have been obtained, respectively, for the both design examples at different frequencies, which shows high accuracy of the proposed ANN design method. Finally, a design of class-E PA is presented using the circuit elements values that, first, extracted by the analyses, and second, predicted by ANN. The calculated drain efficiencies for the designed class-E amplifiers have been obtained equal to 95.5% and 91.2% by using analyses data and predicted data by proposed ANN, respectively. The comparison between the real and predicted values shows a good agreement.

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Section: Results Comparisonmentioning

confidence: 99%

Section: Power Supplymentioning

confidence: 99%

Section: Power Supplymentioning

confidence: 99%

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A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications

et al. 2022

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“…The optimized dimensions of various dual staircase shaped antenna are as follows S L = 34; S W = 25; A 1 = 23; A 2 = 16; A 3 = 8; B 1 = 15.2; B 2 = 2; B 3 = 2; L F = 7.2; W F = 1.5; R 0 = 7.2; G W = 10; T = 1.52 (All units are in millimeters). It needs to be mentioned that other numerical approaches [ 50 ] or customized optimization algorithms, such as particle swarm optimization [ 52 ], neural networks [ 53 ] or other artificial intelligence approaches [ 54 , 55 ] can also be used for further optimization.…”

Section: Design Of Notched Uwb Antennamentioning

confidence: 99%

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

Islam,

Ali,

Awan

et al. 2024

Heliyon

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Design and Modeling of RF Power Amplifiers with Radial Basis Function Artificial Neural Networks

Cited by 2 publications

References 18 publications

A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications

A New Design Method for Class-E Power Amplifiers Using Artificial Intelligence Modeling for Wireless Power Transfer Applications

A parasitic patch loaded staircase shaped UWB MIMO antenna having notch band for WBAN applications

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