A Nonlinear System Identification Method Based on Multi-objective Genetic Programming

Abstract

The invention discloses a nonlinear system recognizing method based on multi-target genetic programming. The method mainly overcomes the shortcomings of low recognizing accuracy and low solving speed of the traditional recognizing method under the condition that the structure of a nonlinear system is unknown. A plurality of target optimization function models optimizing the structure of the nonlinear system and the system parameters are provided. A novel multi-target genetic programming method is adopted to solve the model. A novel evaluation method aiming at multiple optimization targets is provided, a decision making process is blended in the optimization process, calculation of multiple Pareto optimum solutions is not required, and the evaluation process is high in efficiency. The method can effectively excavate the nonlinear relation between a large amount of input-output data, and improve recognizing efficiency and accuracy.

Description

technical field [0001] The field of nonlinear system identification method of the present invention is that nonlinear system is used in automatic industrial control, biomedical data modeling, chemical evolution process, post-disaster management and decision-making, etc., and specifically relates to a nonlinear system identification method based on multi-objective genetic programming. Background technique [0002] Nonlinear system identification is widely used in mining unknown and complex data relationships in various fields, such as: automated industrial control, biomedical data modeling, chemical evolution process, post-disaster management and decision-making, etc. In these problems, a large amount of various data information can be obtained through sensors, networks, etc., and certain nonlinear relationships among these data have a positive effect on improving prediction capabilities, decision-making capabilities, and process optimization capabilities. The nonlinear syste...

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Problems solved by technology

[0004] However, the solution process of genetic programming is a process of searching for the optimal solution in a massive solution space, which usually has the disadvantages of taking a long time and easily falling into a local optimal solution.

In particular, when the structure and parameters of the nonlinear system are unknown, it is easy to fall into the local optimal solution of a certain objective function, and it is not easy to find a global optimal solution in which multiple objective functions are optimal at the same time.

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