| In the design of RF microwave devices,it is necessary to reduce design time and cost while ensuring device performance,and RF microwave device models are typically built using CAD(Computer Aided Design)software.However,in the conventional RF microwave device design,each parameter must be continuously optimized to meet the design specifications,and the entire process is time-consuming and labor-intensive.Because deep neural networks can process highdimensional data and approximate complex nonlinear relationships,the deep neural network modeling method is used in the design of RF modules such as antennas.This paper’s main research work is summarized as follows:Firstly,an improved deep belief network and extreme learning machine(DBN-ELM)surrogate model for designing ultra-wideband(UWB)antennas is proposed to address the problems of many parameters to be optimized,complex network structure and long-term method.This method determines the optimal number of hidden layer nodes of DBN by particle swarm algorithm(PSO),and joins ELM in the training process of DBN to obtain higher feature learning capability and nonlinear function approximation capability,which solves the problem that the network takes a lot of time in global fine-tuning.The improved model can directly predict the overall structural parameters of the fractal antenna and the notch structural parameters of the multiple-input-multiple-output(MIMO)antenna,and the simulation results show that the Sparameters of the antenna are well fitted,indicating that the method has high modeling accuracy and can replace the EM software for optimal design.With the same training samples,the minimum prediction error of DBN-ELM method is 11.87%,which is 68.13% higher than ANN,68.88%higher than MLP,58.60% higher than DBN,53.10% higher than PSO-DBN,41.47% higher than R-DBN,indicating that the proposed DBN-ELM model has higher prediction ability and generalization ability.Then,an inverse modeling method based on gaussian process and deep belief network(GPDBN)is proposed.In the GP-DBN model,a smooth mapping between S-parameters and structural parameters of RF modules can be established to solve the nonlinear relationship between input and output data in RF microwave device design.The GP-DBN inverse model established in this paper is applied to the optimized design of ultra-wideband filter and dual-band microstrip patch antenna.After optimization,the ultra-wideband filter can prevent X-band signal interference,and the dualband microstrip patch antenna can work in WLAN and WIMAX bands,which proves the effectiveness of the model.The prediction error of the GP-DBN model is 22.04%,which is 42.71%,16.01%,and 15.59% better than the three methods of ANN,DBN,and GPLVM,respectively,and the results show that the method has a higher prediction accuracy.The method proposed in this paper can be used for the optimal design of RF modules as a result of the above research,which improves the speed and accuracy of RF module design and enriches the modeling theory and design methods of RF modules.This paper has 32 figures,11 tables and 71 references. |
