Long Short-Term Memory Neural Networks for Modeling Nonlinear Electronic Components

Moradi, Aref; Sadrossadat, Sayed Alireza; Derhami, Vali

doi:10.1109/tcpmt.2021.3071351

Cited by 31 publications

(23 citation statements)

References 34 publications

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“…LSTMs can handle the issue of vanishing gradient encountered during the training procedure of traditional RNNs [149]. LSTM has been introduced into the microwave area for nonlinear device modeling [150] and extrapolation of frequency domain EM responses [151].…”

Section: Deep Neural Networkmentioning

confidence: 99%

“…Further advance in microwave CAD led to the use of deep neural networks with many hidden layers to address more complex microwave modeling problems. Various deep neural network methods have been explored for microwave CAD, such as deep MLP [89], [90], [91], [142], RNN [133], [140], [150], and CNN [74], [77], [85], [151], [172].…”

Section: Deep Neural Network Techniques For Microwave Modelingmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Deep Neural Networkmentioning

confidence: 99%

Section: Deep Neural Network Techniques For Microwave Modelingmentioning

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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“…Most of the prior works on NN-based circuit modeling used discrete-time NN models; examples include the feedforward NN (FNN) with inputs from previous time steps [1], nonlinear autoregressive network with exogenous inputs (NARX) [2], [3], [4], [10], and discrete-time recurrent NN (DTRNN) [7], [8], [9], [11]. All of those models account for the inertia (or "memory") possessed by a circuit.…”

Section: A Capabilities and Limitations Of Prior Workmentioning

confidence: 99%

Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls

Xiong

Yang

Raginsky

et al. 2022

IEEE Trans. Microwave Theory Techn.

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This work advances the application of neural ordinary differential equations (ODEs) to circuit modeling. Prior works primarily utilized the recurrent neural network (RNN), which is a specific type of neural ODE. In this work, the capability of neural ODEs to represent different types of circuits is studied. Stability conditions are presented, both for neural ODEs in a standalone configuration and for neural ODEs with feedback connections, and practical techniques to impose the stability constraints during training are demonstrated. Based on the theoretical and experimental results, this work provides guidance as to when and how an accurate and stable neural ODE circuit model can be generated.

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“…In recent decades, Machine Learning (ML) methods have been widely applied to construct accurate and fast-to-evaluate surrogate models able to reproduce the inputoutput behavior of electromagnetic (EM) structures as a function of deterministic and uncertain parameters [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. In the above scenario, advanced data-driven and ML-based regressions, such as Polynomial Chaos Expansion [1][2][3][4], Support Vector Machine (SVM) regression [5,6], Least-Squares Support Vector Machine (LS-SVM) regression [7], Gaussian Process regression (GPR) [8], and feedforward [9][10][11][12], deep [12,13], convolutional [12] and Long Short-Term Memory (LSTM) [14] neural networks (NNs), have been successful applied to uncertainty quantification (UQ) and optimization in EM applications. The common idea is to adopt the above methods to train a regression model by using a limited number of training samples generated via a set of expensive full-wave or circuital simulations with the so-called computational model.…”

Section: Introductionmentioning

confidence: 99%

Compressed Complex-Valued Least Squares Support Vector Machine Regression for Modeling of the Frequency-Domain Responses of Electromagnetic Structures

Soleimani

Trinchero

2022

Electronics

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This paper deals with the development of a Machine Learning (ML)-based regression for the construction of complex-valued surrogate models for the analysis of the frequency-domain responses of electromagnetic (EM) structures. The proposed approach relies on the combination of two-techniques: (i) the principal component analysis (PCA) and (ii) an unusual complex-valued formulation of the Least Squares Support Vector Machine (LS-SVM) regression. First, the training and test dataset is obtained from a set of parametric electromagnetic simulations. The spectra collected in the training set are compressed via the PCA by exploring the correlation among the available data. In the next step, the compressed dataset is used for the training of compact set of complex-valued surrogate models and their accuracy is evaluated on the test samples. The effectiveness and the performance of the complex-valued LS-SVM regression with three kernel functions are investigated on two application examples consisting of a serpentine delay structure with three parameters and a high-speed link with four parameters. Moreover, for the last example, the performance of the proposed approach is also compared with those provided by a real-valued multi-output feedforward Neural Network model.

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Long Short-Term Memory Neural Networks for Modeling Nonlinear Electronic Components

Cited by 31 publications

References 34 publications

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

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

Neural Ordinary Differential Equation Models of Circuits: Capabilities and Pitfalls

Compressed Complex-Valued Least Squares Support Vector Machine Regression for Modeling of the Frequency-Domain Responses of Electromagnetic Structures

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