Research On RF Circuit Modeling Based On Artifical Neural Network

With the gradual development of wireless communication and RF technologies,the system of RF circuits becomes integrated,the size becomes miniaturized,and the functions become diversified,which leads to more complex structure and higher working frequency of RF circuit.According to Maxwell full wave analysis,the traditional modeling methods will consume more computing resources,increase long computing time and even cannot calculate,which makes the efficiency of circuit simulation analysis lower for designers.Therefore,the traditional RF circuit modeling and analysis methods have gradually failed to meet the requirements of efficient and fast simulation analysis and design of circuits.In order to meet the increasing demand for high-performance RF circuit models,artificial neural network(ANN)based modeling methods have been increasingly valued and studied.Neural networks can map complex non-linear inputoutput relationships.As long as the learning of the neural network model is completed,the model can output the response accurately and quickly without knowing the technical expertise.It is an ideal modeling analysis method.This thesis mainly studies the high performance RF circuit model technology based on the artificial neural network method,focusing on the data preprocessing technology,the neural network modeling method,the neural network overfitting method and the intelligent modeling method.In the process of data preprocessing exploration,the commonly used preprocessing methods such as data conversion,similarity analysis and data dimension reduction are analyzed.Similarity analysis,which combines Pearson correlation coefficient,quantifies the degree of correlation between input variables and output responses,and excludes input variables that are independent of output responses.To reduce the dimension of data,the basic principles and calculation steps of principal component analysis(PCA)and kernel principal component analysis(KPCA)are explored.These methods can not only reduce the dimension of the input vector,but also improve the data quality and reduce the complexity of the network.During the ANN modeling,the common BP,RBF and SVM network structure and calculation steps are deeply learned.In addition,the reasons for and solutions to over-fitting of the neural network are studied,and a new regularization method combining Dropout and L2 regularization is proposed.This method can further eliminate the over-fitting of the model,and its validity is verified by an example.Finally,the intelligent modeling method is proposed,which combine data processing and ANN methods.The PSO algorithm is used to set the initial value of the neural network,which can speed up the convergence of the neural network.A novel intelligent modeling method is presented,which uses data preprocessing to process the original data,extracts the valid information from the original data,and uses particle swarm algorithm to find the global optimal initial value of the neural network.The network only needs a slight adjustment to obtain the final solution,thus achieving an efficient and fast learning process of the neural network.Through the analysis and research of data preprocessing,network model and intelligent optimization algorithm,the artificial neural network method has been further studied in RF circuit modeling,which lays a foundation for establishing a better RF circuit model in the future.