Research On Application Of Intelligent Optimization Algorithm In Nonlinear System Identification

In recent years,the intelligent optimization algorithm has been widely used due to its strong search ability.Now it has become a hot topic of research.In this paper,the intelligent optimization algorithm is applied to the identification of nonlinear systems under the interference of heavy tail noise.First of all,the original Salp Swarm Algorithm(SSA)and Gray Wolf Optimization Algorithm(GWO)are improved.Simulation experiments show that the improved algorithm can effectively improve the convergence accuracy and other performance of the original algorithm.And apply the improved algorithm to the identification of nonlinear MIMO Hammerstein and Hammerstein-Wiener models.The main research contents are as follows:(1)Firstly,a new intelligent optimization algorithm is introduced.The Salp Swarm Algorithm(SSA)is introduced.In view of the defects that the sea squirt algorithm is easy to fall into local optimum and poor convergence accuracy,this paper proposes based on the improvement of the sea squirt following strategy,the introduction of Kent chaotic mapping,etc The improved Chaotic Salp Swarm Algorithm(CSSA)has been verified by test functions.The simulation of the test function verifies the performance of the CSSA algorithm.The CSSA algorithm can effectively meet the accuracy needs of identification research.(2)Next,we introduce a widely used Gray Golf Optimization Algorithm(GWO).In this paper,the Levy Grey Wolf Optimization Algorithm(LGWO)is proposed by introducing adaptive position update formulas,Levy flight strategy and other improvements,and its performance is verified by test function simulation.The simulation results show that LGWO algorithm has convergence accuracy,speed,and stability.All have been greatly improved to meet the requirements of subsequent identification research.(3)The CSSA and LGWO algorithms proposed in this paper are applied to the identification of nonlinear systems under heavy tail noise interference.The system noise in traditional identification research is usually set to white noise,but the actual production process environment is intricate and there are many types of noise.Heavy tail noise is a relatively common noise in industrial processes.The problem of identification when the system noise exhibits heavy-tailed characteristics has not been solved well.Therefore,this paper studies the identification of modular MIMO Hammerstein and Hammerstein-Wiener under the interference of heavy tail noise.The CSSA and LGWO algorithms proposed in this paper are used to transform the identification problem into a parameter optimization problem.The simulation results in Chapters 3 and 4 show that the optimization algorithm proposed in this paper has higher accuracy and smaller deviations.