Design of UWB Antenna Based on Improved Deep Belief Network and Extreme Learning Machine Surrogate Models

In the design of conventional microwave devices, the parameters need to be continuously optimized to meet the desired targets, and the whole process is time-consuming and laborious. As a surrogate model, machine learning is an effective optimization method. However, in the modeling process, the high-dimensional data processing and the complex nonlinear relationship between parameters is a problem to be solved. This paper proposes a deep learning model for designing UWB antennas, which determines the model structure of deep belief network (DBN) by particle swarm algorithm (PSO), and then combines DBN and extreme learning machine (ELM). The proposed model can obtain higher feature learning capability and nonlinear function approximation capability, and has been applied to the optimal design of the whole structure of the fractal antenna and the notch structure of the MIMO antenna, and its S-parameters are well fitted while meeting the requirements of the design targets. The DBN-ELM method obtains the good results when compared with common modeling methods using the same training samples (the root mean square error tested is 11.87% in the fractal antenna and 3.56% in the MIMO antenna). Overall, the proposed DBN-ELM model has higher predictive and generalization capabilities, which can also be used to model more complex antenna structures.