Analog Gross Fault Identification in RF Circuits Using Neural Models and Constrained Parameter Extraction

Viveros-Wacher, Andres; Rayas‐Sánchez, José E.; Brito‐Brito, Zabdiel

doi:10.1109/tmtt.2019.2914106

Cited by 12 publications

(7 citation statements)

References 34 publications

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“…This method can effectively predict the optimal parameters of a tunable matching network and select the range of adaptive transmitting coils. Viveros-Wacher et al [121] have applied an analog gross fault diagnosis method based on ANNs and constrained parameter extraction to analog fault diagnosis. Wei et al [213] have introduced self-attention bidirectional LSTM (Bi-LSTM) networks for radar signal modulation recognition.…”

Section: Further Applications Of Ann In Microwave Cadmentioning

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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Section: Further Applications Of Ann In Microwave Cadmentioning

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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“…Step 3: Renew the inertia weight factor according to formula (4), and update the position and velocity of the particle by equations ( 7) - (9).…”

Section: E Procedures Of the Proposed Ih-pso Algorithmmentioning

confidence: 99%

“…The penalty parameter C and kernel parameter γ of SVM are tuned according to IH-PSO. According to formulas (7) to (9), the position and velocity of the particles are updated to generate a new particle group.…”

Section: B Fault Diagnosis Process Based On Ih-pso Optimized Svmmentioning

confidence: 99%

“…Soft fault, also known as parametric fault [5], [6] is caused by parametric drift, which will not cause damage to the circuit hardware. Due to the continuity of components parameters, the insufficiency of diagnostic information in actual test circuits, as well as the tolerance impact of analog components [7]- [9], parameter fault diagnosis is rather complicated. It is rather troublesome for traditional methods like signal processing or analytical models to meet the needs.…”

Section: Introductionmentioning

confidence: 99%

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Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

et al. 2019

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Because of its excellent small sample learning abilities and simple network structure, support vector machine (SVM) is widely applied in various pattern recognition fields, e.g., face recognition, scene classification, fault diagnosis, etc. Due to the complexity and diversity of analog circuit faults, the diagnosis accuracy and stability of SVM classifier optimized by traditional particle swarm optimization (PSO) are unsatisfactory. Therefore, this paper proposes an improved hybrid particle swarm optimization (IH-PSO) algorithm to optimize SVM, which is applied in the fault diagnosis of analog circuits. Compared with the traditional PSO algorithm, the proposed IH-PSO mainly has three improvements, namely, the oppositionbased learning population initialization, the nonlinear time-varying inertia weight, and the new position updating strategy with a spiral convergence mechanism. The performance of the proposed IH-PSO algorithm is verified by 12 commonly used benchmark functions and experimental results show that the proposed IH-PSO algorithm overcomes the deficiencies of the traditional PSO algorithm, such as slow convergence speed and trapping into local optimums. In addition, to further verify the performance of IH-PSO algorithm, the IH-PSO optimized SVM is applied to solve analog circuits fault diagnosis problems. Extensive experiments are carried out and results indicate that the proposed method has better performances both in diagnosis accuracy and stability compared with that of the traditional method.

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“…Roy et al [12] proposed a way that used a configurable filter structure to test the circuits by Nyquist shape of transfer function, but it must develop a data base with the paramenters of the Nyquist Shape. The work in [13] has proposed a new method that used three layer perceptron to model the effects of injecting gross faults to the circuit, but we need prepare a great number of data for the ANN. Tadeusiewicz and Ossowski [14] based on the linear time-invariant analog electronic circuits proposed a model to multiple soft diagnosis, but the method is limited to linear dynamic circuits.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

Zhang

Guo

et al. 2020

IEEE Access

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In the soft fault diagnosis of nonlinear analog filter circuits, the single feature can't maximally reveal the behaviors hidden in signals. In order to overcome such shortcomings, a fusion algorithm weighted feature from multi-group is proposed. This method use reliefF algorithm to optimize canonical correlation analysis combines support vector machine(RCCA-SVM) for diagnosis. The fault characteristics used in this method are extracted from the time-domain, statistical features and frequency-domain by wavelet packet transform (WPT). And then the CCA algorithm is used to improve the correlation of features according to the weights of the features. Finally, the fusion features are dimension reduced by principal component analysis(PCA), support vector machine(SVM) is the classifier of the diagnosis. The simulations show that the proposed method has a good diagnostic effect on circuit fault diagnosis of non-linear analog circuits. INDEX TERMS Nonlinear analog circuit fault diagnosis, WPT, RCCA-SVM.

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Analog Gross Fault Identification in RF Circuits Using Neural Models and Constrained Parameter Extraction

Cited by 12 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

Fault Diagnosis of Analog Circuits Based on IH-PSO Optimized Support Vector Machine

Nonlinear Soft Fault Diagnosis of Analog Circuits Based on RCCA-SVM

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