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This study proposes neural network‐based iterative inverse solutions for non‐destructive evaluation (NDE) in which vector finite elements (VFEM) represent the forward model that closely models the physical process. The iterative algorithm can eventually estimate the material parameters. Vector finite element method global matrix is stored in a compact form using its sparsity and symmetry. The stored...
The electrolysis of water using a polymer electrolyte membrane (PEM) electrolyzer is a very vital and efficient method of producing hydrogen (H2). The performance of this method can be significantly improved if a photovoltaic (PV) array, with maximum‐power‐point (MPP) tracker, is utilized as an energy source for the electrolyzer. This paper suggests a stand‐alone PV/PEM electrolyzer system to produce...
This paper proposes a new technique to train neural network (NN); with the result, we can solve some real‐world application problems such as microwave components modeling and optimization. Its major advance is achieved in avoiding the testing error falling into local minimum. After the generalization, the ability of three‐layer and four‐layer NN is also checked; our investigations show that four‐layer...
The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy...
A scalable model combining the advantages of the compact model and space‐mapping neural network (SMNN) has been presented to characterize radio‐frequency behaviors of on‐chip spiral inductors. The physics‐based T equivalent circuit model has been used for constructing the proposed scalable SMNN model. All values of the T model elements are fast and accurately extracted based on the mathematical formulations...
Parametric modeling of electromagnetic (EM) behaviors has become important for EM design optimizations of microwave components. This paper provides an overview of recent advances in parametric modeling of microwave components using combined neural network and transfer function (neuro‐TF). Transfer functions are used to represent the EM responses of passive components vs frequency. With the help of...
Communication of interconnected power system components with each other and with control centers is provided by using various communication links. However, these links can cause significant time delays (TD) depending on the types, distance and conditions of the communication channel. In addition to temporary time delays caused by the time constants of the components due to system failures, this article...
The present study introduces a new adaptive control framework that aims to attain exponential stability in complex‐valued neural network systems utilizing memristors while accounting for time‐varying delays. The control issues in systems of this nature are mostly attributed to the presence of memristors and time‐varying latency. To overcome these challenges and achieve stabilization outcomes, a methodology...
High frequency (HF) transmitter implemented with the multioctave broadband power amplifier (PA) is always faced with the problem of interfering harmonics because of the nonlinearity of the PA. With the harmonic‐cancelation digital predistortion (HC‐DPD) scheme, the harmonics at lower frequencies can be canceled by the injected components and the harmonics at higher frequencies can be filtered out...
In the manufacturing process of power amplifier (PA), various defects on the circuit surface will seriously affect the circuit performance and its operation. To solve the above problems, this article proposes a circuit defect detection method based on improved patch‐based support vector data description algorithm (Patch SVDD), which uses contrastive learning to enhance the feature extraction ability...
Radiation patterns of a phased array tend to be affected by random excitation errors. In this paper, a unified neural network (UNN) architecture is presented to study the statistical characteristics of radiation patterns. Benefited from the inherent symmetry of the array manifolds and efficient data preprocessing, the UNN architectures are almost identical for planar arrays consisting of 4–10 000...
Electromagnetic (EM) parametric modeling has become significant for EM designs of microwave devices. This paper outlines recent advances in hybrid format‐based neuro‐transfer function (TF) techniques for EM parametric modeling of microwave devices. To solve the problem of high‐sensitivity, a novel decomposition approach is discussed to develop a rational‐based neuro‐TF model of EM behavior of microwave...
Using a large number of power amplifiers brings serious nonlinear distortion to 5G communication systems, which becomes a challenging problem for conventional digital predistortion (DPD) to linearize signals. To address this challenge, we propose a digitally assisted nonlinearity suppression architecture for applications in multiple antenna arrays. In this architecture, we use an auxiliary array to...
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