Abstract
To enhance the efficiency of antenna optimization, surrogate model methods can usually be used to replace the full-wave electromagnetic simulation software. Broad learning system (BLS), as an emerging network with strong extraction ability and remarkable computational efficiency, has revolutionized the conventional artificial intelligence (AI) methods and overcome the shortcoming of excessive time-consuming training process in deep learning (DL). However, it is difficult to model the regression relationship between input and output variables in the electromagnetic field with the unsatisfactory fitting capability of the original BLS. In order to further improve the performance of the model and speed up the design of microwave components to achieve more accurate prediction of hard-to-measure quality variables through easy-to-measure parameter variables, the conception of auto-context (AC) for the regression scenario is proposed in this paper, using the current BLS training results as the prior knowledge, which are taken as the context information and combined with the original inputs as new inputs for further training. Based on the previous prediction results, AC learns an iterated low-level and context model and then iterates to approach the ground truth, which is very general and easy to implement. Three antenna examples, including rectangular microstrip antenna (RMSA), circular MSA (CMSA), and printed dipole antenna (PDA), and 10 UCI regression datasets are employed to verify the effectiveness of the proposed model.
Highlights
As is known to all, electromagnetic simulation software (EMSS) such as high-frequency structure simulator (HFSS) and computer simulation technology (CST) is most commonly used in the optimization design of electromagnetic devices, which can obtain high-precision results, along with high computational and time cost. erefore, the use of surrogate models instead of EMSS for evaluating the fitness of electromagnetic components can save much optimization time, which is currently a hot topic in electromagnetic optimization design
Many popular modeling methods have been widely used like Gaussian process (GP) [1, 2], backpropagation (BP) [3], artificial neural network (ANN) [4,5,6], support vector machine (SVM) [7, 8], extreme learning machine (ELM) [9, 10], kernel ELM (KELM) [11], and so on
Thanks to the fast incremental learning algorithm [17], which is applied to Broad learning system (BLS), when faced with newly added samples and hidden nodes, the system can be updated incrementally without rebuilding the entire network from scratch. e construction of BLS is International Journal of Antennas and Propagation based on the theory of random vector functional-link neural network (RVFLNN) [18, 19]; instead of directly bringing the original inputs into the network, BLS first maps them into feature nodes and imitates the practice of RVFLNN, that is, nonlinearly transforming them into enhancement nodes, and these two parts together constitute the hidden layer of BLS
Summary
As is known to all, electromagnetic simulation software (EMSS) such as high-frequency structure simulator (HFSS) and computer simulation technology (CST) is most commonly used in the optimization design of electromagnetic devices, which can obtain high-precision results, along with high computational and time cost. erefore, the use of surrogate models instead of EMSS for evaluating the fitness of electromagnetic components can save much optimization time, which is currently a hot topic in electromagnetic optimization design. A novel k-means clustering algorithm [25] based on a noise algorithm is developed, which solves the problem of determining the number of clusters and sensitively initializing the center cluster in the traditional k-means clustering algorithm It is an iterative clustering analysis algorithm, which starts the iteration according to the current clustering results. Still, coupled multistable stochastic resonance (CSMR) [26], adaptively optimizes and determines the system parameters of SR by using the output signal-tonoise ratio and seeker optimization algorithm and feeds the preprocessed signal into CMSR for further training Illuminated by these above approaches, we further propose auto-context BLS (ACBLS) as another version of MABLS, and the context is defined as the predicted values of the current model in regression problem. MABLS is applied for antenna optimization, and this paper is a continuation of this research
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