Inverse Problem Research Based On Chua's Circuit

At present,the use of data to determine systems,especially complex large-scale systems,has been a concern of people.In real life,people can only get data from it,but it is not clear about the system dynamics structure that produce these data or some parameters in the dynamic equation.Inversion of complex system dynamics structure or dynamic equation parameters from the obtained data is of great significance to the study of more topics such as inferring gene regulatory networks from the expression data in biological networks,predicting the spread of information and viruses in social networks.Because of the non-linearity and noise of the actual system,some data are sometimes missing,which makes it challenging to reconstruct the non-linear dynamic equation using the data.In order to solve the above problems,this paper chooses Chua's circuit as the research object,which has a simple structure but can show abundant non-linear phenomena.The dynamic parameters of the analog Chua's circuit,the single experimental Chua's circuit with noise and the coupled experimental Chua's circuit with noise are identified by using the high-order correlation computations(HOCC).The results show that the method is effective for parameter identification under the condition that the inductance current of Chua's circuit is unknown and known,and has good robustness to noise.The results of parameter identification and the relative difference of circuit calibration parameters can be within 20%.This research verifies the effectiveness of HOCC from the actual system,solves the parameter identification problem of system with missing variables in noise and non-linearity,and has certain physical significance.It can provide reference for device parameter measurement in Chua's circuit,and can be further applied to other network structure parameter identification experiments.