Research On Identification Algorithms Of Blocking Nonlinear Systems

As one of the key issues of system and control science, system identification has been widely applied to the design and analysis of control system, and has been gone deep into many fields of science and technology, such as biology, physiology, medicine, sociology, and so on. Consequently, system identification becomes one of the current very active subjects, attracting a large number of scientific and technical personnel for their theoretical study to examine the practical problems in different application possibilities.In research on the method of system identification, because many engineering objects are very complex, the identification methods of nonlinear systems show remarkable advantage in the analysis of the engineering object. However, because of the inherent complexity and diversity of nonlinear systems, the current research on the nonlinear far not reached the degree of maturity; it has not been fully revealed that the "essence problem" of nonlinear was contained behind the complex phenomenon. Thereby, the complex nonlinear object recognitions are still not well solved via the traditional identification methods; the identification of nonlinear system was the main topics in the current international identification fields.For the identification of nonlinear system, one of the difficulties is short of unified mathematical model to description of various nonlinear system features. To this end, one should study on different specific problems. Consequently, in this paper, the identification algorithms of blocking nonlinear systems are discussed and studied, and expected good results in practical applications. The main research work is as follows:1. The identification problem of nonlinear system was studied based on a particle swarm optimization algorithm. The basic idea is that identification method was given by a particle swarm optimization algorithm, after the nonlinear system was blocked. That is to say, first of all, the idea of the method employed a system model composed with classical models so as to transform the system structure identification problem into a combinational problem. And then, a particle swarm optimization algorithm was adopted to implement the identification on the system structure and parameters. Simulation results showed the rationality and effectiveness of the presented method.2. The parameter identification method of blocking nonlinear model with two-segment piecewise nonlinearities was presented. Its basic idea is that:Firstly, expressing the output of the blocking nonlinear models as a regressive equation in all parameters based on the key term separation principle and separating key term from linear block and nonlinear block. Secondly, the unknown true outputs of the information vector are replaced with the outputs of an auxiliary model; the unknown internal variables and the unmeasured noise terms are replaced with the estimated internal variables and the estimated residuals, respectively. Accordingly, the problem of nonlinear system identification was cast as function optimization over parameter space, and then, a particle swarm optimization algorithm was adopted to solve the optimization problem. Finally, simulation results showed the effectiveness of the proposed method.3. A blind identification algorithm to a blocking nonlinear model, where the nonlinear block is a linear subsystem is followed by a memoryless nonlinear gain, was investigated. When the input signal of system didn't adopt a Gaussian random signal, the identification process with the input signal was changed into the one without input signal using the first-order moment of the cyclostationary input signal and the inverse nonlinear mapping of the nonlinear model. And all internal variables were recovered based on the output signal. Then, parameters of the model were obtained through support vector machine linear regression algorithm and higher order cumulants method. Finally, the efficiency and stability of the presented algorithm were demonstrated by simulation examples.4. For a class of multi-input single-output blocking nonlinear model, the model identification problem is equivalent to the nonlinear minimization problem with the estimated parameters as the optimized variables. The basic idea of the method is that the problem of the nonlinear system identification was changed into an optimization problem in parameter space and a particle swarm optimization algorithm was then adopted to solve the optimization problem. In order to enhance the identification performance of the particle swarm optimization algorithm, a hybrid particle swarm optimization algorithm was also adopted. Finally, simulation results showed the effectiveness and practicality of the proposed identification method.5. A blind identification algorithm for the single-input single-output sampled blocking nonlinear Hammerstein-Wiener model was proposed. When the input signal of the system didn't adopt a white noise signal, by a blind signal disposal approach, all internal variables were recovered solely based on the output signal. Then, parameters of linear and nonlinear parts of the model were obtained through support vector machine linear regression algorithm. Finally, in simulation experiments, compared with using least square method, the effectiveness of the presented algorithm was illustrated.6. In order to improve the identification performance of the wavelet neural network for the nonlinear system, the parameters of a BP wavelet neural network were trained via an improved particle swarm optimization algorithm to obtain optimal values to achieve the purpose of identification for the nonlinear system. In numerical simulation, compared with using standard particle swarm optimization algorithm, the results showed that the presented algorithm was obviously improved in the convergence, stability, and so on.