Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm

Fan, Xiaohong; Tian, Yubo; Zhao, Yi

doi:10.1155/2019/2176518

Cited by 10 publications

(8 citation statements)

References 29 publications

(26 reference statements)

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“…It is suitable for solving complex problems such as high dimensions, nonlinearity and small samples. [15][16][17] Compared with the popular NN, GP is easier to model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of antenna resonant frequency based on co‐training of semi‐supervised Gaussian process with different kernel functions

Gao

Tian

Chen

2021

Int J RF Microw Comput Aided Eng

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Usually, traditional machine learning (ML) methods use only labeled samples for learning. However, in practical problems including electromagnetic optimization design, the acquisition cost of labeled samples is relatively high.Obtaining label training samples is the most time-consuming part, so how to use relatively few label samples for training to obtain a high-precision surrogate model is a hot topic. This study proposes a co-training algorithm of semisupervised Gaussian Process (GP) with different kernel functions, based on the differences between these two different GP models. The algorithm is conducted by a small number of labeled samples in combination with unlabeled samples, so as to continuously improve the accuracy of the models. Stop criteria is set in advance to control the number of unlabeled samples introduced, preventing the accuracy of the model reduced by introducing too much unlabeled samples. Furthermore, the proposed algorithm is evaluated by benchmark functions and resonant frequency modeling problems of two different antennas. Results show that the proposed GP model has good fitting effects on the benchmark functions. For the problems of resonant frequency modeling, in the case of the same labeled samples, its predictive ability is better than that of the traditional supervised learning (SL) method.

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“…It is suitable for solving complex problems such as high dimensions, nonlinearity and small samples. [15][16][17] Compared with the popular NN, GP is easier to model.…”

Section: Introductionmentioning

confidence: 99%

Modeling of antenna resonant frequency based on co‐training of semi‐supervised Gaussian process with different kernel functions

Gao

Tian

Chen

2021

Int J RF Microw Comput Aided Eng

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show abstract

“…erefore, using a surrogate method instead of HFSS to evaluate the fitness of electromagnetic devices can save greatly optimization time, which is a hot topic in electromagnetic optimization design. Many researchers have proposed lots of surrogate methods, such as artificial neural network (ANN) [2,3], support vector machine (SVM) [4,5], kernel extreme learning machine (KELM) [6,7], and Gaussian process (GP) [8,9].…”

Section: Introductionmentioning

confidence: 99%

Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

et al. 2020

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For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D. Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high. This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required. The proposed GP model consists two parts: initial training and self-training. In the process of initial training, a small number of labeled samples obtained by full wave electromagnetic simulation are used for training the initial GP model. Afterwards, the trained GP model is copied to another GP model in the process of self-training, and then the two GP models will update after crosstraining with different unlabeled samples. Using the same test samples for testing and updating, a model with a smaller error will replace another. Repeat the self-training process until a predefined stopping criterion is met. Four different benchmark functions and resonant frequency modeling problems of three different microstrip antennas are used to evaluate the effectiveness of the GP model. The results show that the proposed GP model has a good fitting effectiveness on benchmark functions. For microstrip antennas resonant frequency modeling problems, in the case of using the same labeled samples, its predictive ability is better than that of the traditional supervised GP model.

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“…erefore, it often uses high-accuracy discrete data sets to ensure that the model has sufficient prediction accuracy. Meanwhile, for calculation with the same amount of training data, GP requires more time [11].…”

Section: Introductionmentioning

confidence: 99%

“…Simultaneously, particle swarm optimization (PSO) algorithm is used to optimize the parameters of the DGP network model when training the model. Considering the current research situation, the application of PSO algorithm to optimize CNN and GP has been very mature [15,16]. Comparing with the traditional error backpropagation (BP) optimization of CNN, PSO is very flexible in optimizing model parameters [17].…”

Section: Introductionmentioning

confidence: 99%

Antenna Optimization Design Based on Deep Gaussian Process Model

Zhang

Tian

Xie

2020

International Journal of Antennas and Propagation

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When using Gaussian process (GP) machine learning as a surrogate model combined with the global optimization method for rapid optimization design of electromagnetic problems, a large number of covariance calculations are required, resulting in a calculation volume which is cube of the number of samples and low efficiency. In order to solve this problem, this study constructs a deep GP (DGP) model by using the structural form of convolutional neural network (CNN) and combining it with GP. In this network, GP is used to replace the fully connected layer of the CNN, the convolutional layer and the pooling layer of the CNN are used to reduce the dimension of the input parameters and GP is used to predict output, while particle swarm optimization (PSO) is used algorithm to optimize network structure parameters. The modeling method proposed in this paper can compress the dimensions of the problem to reduce the demand of training samples and effectively improve the modeling efficiency while ensuring the modeling accuracy. In our study, we used the proposed modeling method to optimize the design of a multiband microstrip antenna (MSA) for mobile terminals and obtained good optimization results. The optimized antenna can work in the frequency range of 0.69–0.96 GHz and 1.7–2.76 GHz, covering the wireless LTE 700, GSM 850, GSM 900, DCS 1800, PCS1900, UMTS 2100, LTE 2300, and LTE 2500 frequency bands. It is shown that the DGP network model proposed in this paper can replace the electromagnetic simulation software in the optimization process, so as to reduce the time required for optimization while ensuring the design accuracy.

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Optimal Design of Multiband Microstrip Antennas by Self-Renewing Fitness Estimation of Particle Swarm Optimization Algorithm

Cited by 10 publications

References 29 publications

Modeling of antenna resonant frequency based on co‐training of semi‐supervised Gaussian process with different kernel functions

Modeling of antenna resonant frequency based on co‐training of semi‐supervised Gaussian process with different kernel functions

Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

Antenna Optimization Design Based on Deep Gaussian Process Model

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