Bayesian-Assisted Multilayer Neural Network Structure Adaptation Method for Microwave Design

Cui, Jinyuan; Feng, Feng; Zhang, Jianan; Zhu, Lin; Zhang, Qi‐Jun

doi:10.1109/lmwc.2022.3201123

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…Back‐propagation is to define a cost function, and calculate and update the relationship between the objective function and the weights and biases parameters of neuron units within each layer through the way of backward derivative. This progress follows the chain rule [29]. Consider one supervised learning scenario where the l ‐dimension input vector of single‐phase earth fault features, x l is associated with the b ‐dimension target vector, y b with m input‐target fault/non‐fault pairs in total.…”

Section: Deep‐learning Enhanced Detection Modelmentioning

confidence: 99%

Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Su,

Zhang,

Zhang

et al. 2024

IET Generation Trans & Dist

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The rapid and accurate identification of single‐phase earth fault is of prime concern for many scholars from industry and academia, because its fault characteristics are extremely weak while the related potential hazard is extremely severe. In this context, this paper borrows from Deep Learning and in turn innovatively puts forward a multilayer perceptron (MLP)‐based single‐phase earth fault detection model augmented with kernel principal component analysis (KPCA). First, KPCA is applied to building fault feature extraction engineering via the transformation of fault features from low‐dimensional linear indivisible space to high‐dimensional one. Second, a MLP‐based model is used for basic model reference where many optimization strategies together with dropout technique are introduced and custom‐designed in tuning this model to achieve the best detection accuracy and convergence characteristic. Numerical studies demonstrate the superiority of the proposed KPCA‐MLP‐based single‐phase earth fault detection model.

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Section: Deep‐learning Enhanced Detection Modelmentioning

confidence: 99%

Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Su,

Zhang,

Zhang

et al. 2024

IET Generation Trans & Dist

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“…The interconnect structure and microwave devices are key components for critical signal transmission, and the quality of their SI directly determines the performance of the system [ 28 ]. The researchers achieved the goal of high-quality signal transmission by modeling, simulating and optimizing the structure of the interconnection structure and microwave device [ 29 , 30 , 31 , 32 , 33 ]. Approaches to model building fall into two broad categories.…”

Section: Introductionmentioning

confidence: 99%

Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems

Shan

Wang

et al. 2023

Micromachines

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Microsystems are widely used in 5G, the Internet of Things, smart electronic devices and other fields, and signal integrity (SI) determines their performance. Establishing accurate and fast predictive models and intelligent optimization models for SI in microsystems is extremely essential. Recently, neural networks (NNs) and heuristic optimization algorithms have been widely used to predict the SI performance of microsystems. This paper systematically summarizes the neural network methods applied in the prediction of microsystem SI performance, including artificial neural network (ANN), deep neural network (DNN), recurrent neural network (RNN), convolutional neural network (CNN), etc., as well as intelligent algorithms applied in the optimization of microsystem SI, including genetic algorithm (GA), differential evolution (DE), deep partition tree Bayesian optimization (DPTBO), two stage Bayesian optimization (TSBO), etc., and compares and discusses the characteristics and application fields of the current applied methods. The future development prospects are also predicted. Finally, the article is summarized.

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An Efficient Batch-Adjustment Algorithm for Artificial Neural Network Structure Adaptation and Applications to Microwave Modeling

Liu

Zhang

et al. 2023

IEEE Microw. Wireless Tech. Lett.

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Bayesian-Assisted Multilayer Neural Network Structure Adaptation Method for Microwave Design

Cited by 7 publications

References 16 publications

Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Kernel‐PCA‐based single‐phase earth fault detection model using multilayer perceptron in deep learning

Application and Prospect of Artificial Intelligence Methods in Signal Integrity Prediction and Optimization of Microsystems

An Efficient Batch-Adjustment Algorithm for Artificial Neural Network Structure Adaptation and Applications to Microwave Modeling

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