A neural-network method for the analysis of multilayered shielded microwave circuits
In this paper, a neural-network-based method for the analysis of practical multilayered shielded microwave circuits is presented. Using this idea, a radial basis function neural network (RBFNN) is trained to approximate the space-domain multilayered media boxed Green's functions used in the integral-equation (IE) method. Once the RBFNN has been trained, the outputs of the neural network (NN) replace the exact Green's functions, during the numerical solution of the IE. The computation of the RBFNN output values is very fast in comparison with the numerical methods used to calculate the exact Green's functions. This paper describes two novel strategies for efficiently training the RBFNN. In the first strategy, the input space of the RBFNN is divided into several spatial and frequency regions. The spatial subdivision is extended for the first time to both observation and source regions. In addition, the subdivision of the observation points regions is applied in a novel manner to the whole cross section of the metallic box. The second strategy combines the above region subdivision with an adaptive selection of the neurons variances in each region. The accuracy and the computational gain achieved with the NN method proposed makes possible the implementation of computer-aided-design tools that can be used for the analysis and design of integrated shielded microwave circuits (e.g., monolithic microwave integrated circuit devices) on a real-time basis