Small Population-Based Particle Swarm Optimization And Its Applications In Nonlinear Systems Identification

Translational medicine gets fast development in recent years. It combines the basic medicine and the clinical medicine with information technology for the disease prediction, prevention, diagnosis and treatment of help. An important aspect is the computer aided diagnosis. A large number of different types of information in the clinical provide individualized treatment strategies through the computer information processing, so as to improve the efficiency of the diagnosis of disease. It is a necessary link to estimate the medical parameters according to the medical model and clinical testing data and using the system identification method for individual patients. One of the important problems is to choose a appropriate optimization algorithm.Hepatitis B is one of the most serious infectious diseases that affect the people's health of the world. However, the traditional medical analysis is very difficult to have a general grasp on each patient's condition not to mention predict disease's development. With development of medical technology, people are trying to build a dynamic model of hepatitis B virus (HBV dynamics model). Through the parameter identification the model simulation important indexes of Hepatitis B of patient. It may make a prediction about the progress of disease and pesticide effect. This result provides valuable information for doctors and shortens the time of therapy. But this is a nonlinear systems identification problem. Results of traditional system identification methods for nonlinear system identification problems are bad. Existing algorithms like quadratic programming method can identify parameter's number is very limited and it has the limitations of stagnation and it is heavily dependent on initial values of the parameters. Current methods can only identify four parameters at most. With the continuous development of the area of intelligent control, the degree of nonlinearity becomes higher and higher.This dissertation studies the small population-based particle swarm optimization algorithm (SPPSO) in nonlinear system identification according to the dynamic model of Hepatitis B and the clinical testing dynamic data and provides a new auxiliary means. The main works are:The effectiveness of the genetic algorithm is compared to SPPSO in nonlinear system identification. SPPSO has been found to have faster convergence speed in keeping the accuracy. So it is more suitable to the need of the clinical analysis. SPPSO is an optimization technique for locating the global optimum. SPPSO is easy to realize, quick convergence and effective. It can greatly reduce the time and resource costs in the processing of large data quantity of large-scale population problem. So, in system identification, especially in highly nonlinear systems is more meaningful. And this kind of complex system is typical in medical system. SPPSO is used in solving hepatitis B virus dynamics (HBV) model. It has good research and practical value.According to the drug effect lag effect, the delay parameter is identified as a parameter which will be estimated, realized the Hepatitis B virus delay model identification using SPPSO and the algorithm which is suitable to solve the delay differential equation.Pay attention to the time-varying characteristics of the medical parameters, the linear combination of the orthogonal polynomial is used to approximate the time varying parameter, which is means that the infinite dimensional problem is transformed into finite dimensional problems. And then the identification of the Hepatitis B virus time-varying model is realized using SPPSO.This dissertation provides a new method of parameter identification for Hepatitis B virus dynamics model. This method could be spread to the general nonlinear system identification. The Research result provides a "soft measurement" method for medical measurement:if the medicine parameter can't be detected directly or the testing cost is very high, we can choose " the second variables " which is easy to detect, set up the mathematical model between "the secondary variables" and the variables which we wish to detect (in infectious diseases, the general form is a nonlinear differential equations), the variables which we wish to detect can be estimated through the system identification. It has good research and practical value.