Efficient extreme learning machine with transfer functions for filter design

Xiao, Liye; Shao, Wei; Liang, Tu-Lu; Wang, Bing‐Zhong

doi:10.1109/mwsym.2017.8058624

Cited by 16 publications

(11 citation statements)

References 7 publications

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“…ELM is emerging as a new developmental milestone in artificial neural networks. They have been widely applied in microwave millimeter-wave devices/circuits, such as filter design [ 24 ], behavior modeling of power amplifiers [ 25 ], etc. The relationship between the input and output of an RF amplifier is strongly nonlinear [ 25 ], and the ELM has a strong non-linear learning capability [ 18 ].…”

Section: Modeling Processmentioning

confidence: 99%

Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine

2022

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The amplifier is a key component of the radio frequency (RF) front-end, and its specifications directly determine the performance of the system in which it is located. Unfortunately, amplifiers’ specifications degrade with temperature and even lead to system failure. To study how the system failure is affected by the amplifier specification degradation, it is necessary to couple the amplifier specification degradation into the system optimization design. Furthermore, to couple the amplifier specification degradation into the optimal design of the system, it is necessary to model the characteristics of the amplifier specification change with temperature. In this paper, the temperature characteristics of two amplifiers are modeled using an extreme learning machine (ELM), and the results show that the model agrees well with the measurement results and can effectively reduce measurement time and cost.

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Section: Modeling Processmentioning

confidence: 99%

Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine

2022

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“…When the behavior of the problem exhibits high nonlinear phenomena or contains sharp variations, wavelet neural networks (WNNs) can be used, since the localized nature of their hidden neurons makes it easier to train and obtain a promising model accuracy [15], [21], [24]. As a single-hidden layer FFNN, the extreme learning machine (ELM) is found to have a fast learning speed and good performance in EM parametric modeling when the training dataset is not too large [127], [128], [129].…”

Section: A Feedforward Neural Networkmentioning

confidence: 99%

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Feng

Jin

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This article presents an overview of artificial neural network (ANN) techniques for a microwave computer-aided design (CAD). ANN-based techniques are becoming useful for performing forward/inverse modeling for active/passive components to enhance a circuit design. With measured or simulated data of microwave devices, ANNs can be trained to learn relevant microwave relationships, which are, otherwise, computationally expensive or for which efficient analytical formulas are not available. Fundamental concepts of the ANN structure and training, such as feedforward neural networks (FFNNs), recurrent neural networks (RNNs)/dynamic neural networks (DNNs)/time-delay neural networks (TDNNs), deep neural networks, and neural network training and extrapolation, are described. Knowledgebased neural networks (KBNNs) are described for improving the accuracy and reliability of modeling and design optimization. Various advanced ANN techniques, such as neuro-transfer function (neuro-TF) modeling, neural network inverse modeling, and deep neural network modeling, are discussed. The existing and emerging applications of ANN in microwave CAD are identified, such as electromagnetic (EM)/multiphysics modeling, modeling of nonlinear circuits and transistors, filter design, very largescale integration (VLSI) interconnects, oscillator, transmitter and receiver modeling, and CAD applications in such as gallium nitride (GaN) high electron-mobility transistor (HEMT), wireless power transfer (WPT), microelectromechanical system (MEMS), and substrate-integrated waveguide (SIW).

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“…ELM is a feed-forward neural network, and its structure is shown in figure 1. Without loss of generality, the ELM has faster learning speed and stronger generalization ability compared with other neural networks [11].…”

Section: Proposed Gwo-elm Algorithmmentioning

confidence: 99%

Optimization Method of Microwave Devices Based on Improved Extreme Learning Machine

Cong

Chen

Sun

et al. 2022

J. Phys.: Conf. Ser.

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With the rapid development of modern wireless technology, it is necessary to continuously optimize microwave devices to meet the higher requirements of communication systems. In this paper, we propose a microwave device optimization method based on extreme learning machine (ELM) and gray wolf optimizer (GWO). we adopt GWO to optimize the parameters of ELM and establish the mapping relationships between the microwave device design parameters and their responses. According to the inverse mapping of expected response, the expected design parameters of microwave devices are obtained. The numerical results show that the proposed method can improve the design efficiency of microwave devices and realize automatic optimization design compared with the existing method.

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Efficient extreme learning machine with transfer functions for filter design

Cited by 16 publications

References 7 publications

Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine

Modeling of Key Specifications for RF Amplifiers Using the Extreme Learning Machine

Artificial Neural Networks for Microwave Computer-Aided Design: The State of the Art

Optimization Method of Microwave Devices Based on Improved Extreme Learning Machine

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