Bayesian-Based Automated Model Generation Method for Neural Network Modeling of Microwave Components

Cui, Jinyuan; Feng, Feng; Na, Weicong; Zhang, Qi‐Jun

doi:10.1109/lmwc.2021.3087163

Cited by 20 publications

(4 citation statements)

References 10 publications

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“…The key to Bayesian network inference is to infer the possibility of results based on the events that have occurred, which is manifested in the application of the statistical knowledge of the Yeesian network to calculate the relevant results with the support of the existing parameters (probability distribution) of the Bayesian network nodes [14][15][16].…”

Section: Relevant Conclusion Of Bayesian Network Inferencementioning

confidence: 99%

Formulation and Implementation of a Bayesian Network-Based Model

Shi

2023

Int. J. Appl. Inf. Manag.

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At present, Bayesian networks lack consistent algorithms that support structure establishment, parameter learning, and knowledge reasoning, making it impossible to connect knowledge establishment and application processes. In view of this situation, by designing a genetic algorithm coding method suitable for Bayesian network learning, crossover and mutation operators with adjustment strategies, the fitness function for reasoning error feedback can be carried out. Experimental results show that the new algorithm not only simultaneously optimizes the network structure and parameters, but also can adaptively learn and correct inference errors, and has a more satisfactory knowledge inference accuracy rate.

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Section: Relevant Conclusion Of Bayesian Network Inferencementioning

confidence: 99%

Formulation and Implementation of a Bayesian Network-Based Model

Shi

2023

Int. J. Appl. Inf. Manag.

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“…A robust automated model generation (AMG) algorithm [158], [159] for neural network training can be used to significantly reduce the intensive human effort demanded by the conventional step-by-step neural modeling approach. The algorithm inherently distinguishes between smooth and nonlinear regions of model behavior.…”

Section: F Neural Network Training and Data Generationmentioning

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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“…With the rapid advancements in artificial intelligence, the utilization of artificial neural networks (ANNs) has emerged as a powerful tool for facilitating filter designs. Researchers have extensively explored various neural network architectures for parameterized electromagnetic (EM) modeling [21,22], as well as graphical modeling with fragment-type structures for planar microstrip circuits [23][24][25]. Benefiting from these advanced techniques, designers have achieved smaller filter sizes and optimized parameter indexes.…”

Section: Introductionmentioning

confidence: 99%

Generative Model for Dual-Band Filters Based on Modified Complementary Split-Ring Resonators

Zhang,

Xu,

Zhang

2024

Electronics

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This article presents a generative model for the inverse design of dual-band filters based on a type of modified complementary split-ring resonator (CSRR). It consists of a series of convolutional neural networks that incorporate the conditional deep convolutional generative adversarial network (GAN) technique. The filters are designed by etching the modified CSRRs on the surface of substrate-integrated waveguides. This design allows us to achieve two passbands with a compact size. In this GAN-based generative model, the CSRRs are represented as two-dimensional matrices. Each matrix corresponds to a training sample of the designed filter, and its S-parameters are extracted through an HFSS simulation. Both the matrices and the S-parameters are fed into the model as the training datasets. Different CSRRs with various sizes are employed for a wider applicable frequency band. Normalized matrices and normalized S-parameters are utilized to simplify the complex generative model resulting from the variations in CSRR sizes. The effectiveness of the generative model is validated through four design examples of dual-band filters, with their center frequencies located within 5 to 18 GHz. The inference time for each design is approximately 18.5 min. The measurement results of the fabricated filters are in good agreement with the simulation ones.

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Bayesian-Based Automated Model Generation Method for Neural Network Modeling of Microwave Components

Cited by 20 publications

References 10 publications

Formulation and Implementation of a Bayesian Network-Based Model

Formulation and Implementation of a Bayesian Network-Based Model

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

Generative Model for Dual-Band Filters Based on Modified Complementary Split-Ring Resonators

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