Optimization design method for microwave circuit

Abstract

The invention discloses an optimization design method for a microwave circuit, and the method comprises the following steps: obtaining a sample model design parameter through LHS, and obtaining a corresponding sample response through a Matlab-HFSS joint simulation technology; calculating correlation coefficients of all sample responses and target responses, selecting the sample with the maximum correlation coefficient as an optimization sample, and other samples serving as training samples; training the ELM by using the training sample, predicting design parameters responding to the optimized sample, and optimizing an input weight and a threshold value of the ELM by using BSO; and establishing a mapping relationship between the microwave circuit model design parameters and the response by using the ELM after optimizing the input weight and the threshold, training by using all the training samples in the training process, and predicting the model design parameters corresponding to the target response in the prediction process. According to the method, the training and prediction quality of the neural network is improved, the number of required training samples is reduced, the time required for optimization design of the microwave circuit is shortened, the automation of the optimization design of the microwave circuit is realized, and the optimization design efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to an optimization design method for microwave circuits. Background technique [0002] With the rapid development of modern wireless communication technology, communication systems have higher and higher requirements for microwave circuits. In order to meet the different needs of communication systems, it is necessary to continuously optimize the design of microwave circuits. In order to improve the design efficiency of microwave circuits and reduce the time cost of design, it has become an inevitable trend to use algorithms to design them. [0003] In recent years, with the development of computers, their computing power has been greatly improved. Artificial Neural Network (ANN) has re-entered people's field of vision, and has attracted more and more attention from scholars, and it has been widely used. application. ANN is a mathematical model that simulates the ac...

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Problems solved by technology

[0008] However, when using the traditional ANN method to optimize the design, it is necessary to use the gradient descent method to perform a large number of iterations to optimize its network parameters. There are generally problems such as poor learning ability, slow training speed, and low prediction accuracy, which lead to complex time in practical applications. High degree, need to waste a lot of time and labor cost

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