Non-linear system identification method based on particle swarm

Abstract

The invention discloses a non-linear system identification method based on a particle swarm, which mainly aims at the disadvantages of descending of the astringency property caused by high sensibility of additive noise by the traditional identification method. The implementation steps are: setting the highest order, a maximal memory length and a coefficient vector of a non-linear system; determining a constraint condition of an identification problem and designing an object function; setting a parameter of the particle swarm, and generating an initial speed matrix and a spatial position matrix of the particle swarm; calculating the optimum solution and the optimum fitness of the particle swarm according to the spatial position matrix and the object function of the particle swarm; updatingthe particle swam speed and spatial position matrix according to particle swarm speed updating formula and the spatial position; and finishing identification if the particle swarm optimum fitness or times of iteration satisfies the astringency condition. By the invention, the sensibility of the additive noise can be reduced, and the identification performance of the non-linear system under the additive noise condition can be improved.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to an identification method for a nonlinear system, which can be applied to predistortion and blind equalization in a wireless communication system. Background technique [0002] With the development of science and technology, people gradually realize that using mathematical models to study the changing laws of various systems or things has incomparable advantages over other model methods. The principle of system identification is to study how to use known conditions to obtain mathematical models. unknown parameters. [0003] In real life, most systems are nonlinear, that is, there are nonlinear changes between input and output data, which belong to nonlinear systems. Therefore, the study of linear system identification algorithm is only of research significance, but not of practical significance. Therefore, people gradually shift the research focus to solve the proble...

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Problems solved by technology

Among them, the LMS algorithm, NLMS algorithm and RLS algorithm are iterative adaptive algorithms, which use the gradient minimum principle to search. Since the search direction of the iterative process is deterministic, these algorithms are very sensitive to additive noise, so the stability is poor. LS algorithm needs Solving the inverse of the matrix is ​​too complex to be practically applicable. Therefore, how to improve the performance of the identification algorithm for the identification of nonlinear system parameters under additive noise has become an important issue of concern to researchers.

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