The Research Of Nonlinear System Identification Based On Gene Expression Programming

With the development of science and technology, modern industrial processes become more complex, and detailed understanding of complex object characteristics also becomes increasingly difficult. The object model obtained through system identification is the basis of its analysis and control. In recent decades, system identification methods have become important tools of studying complicated nonlinear systems, and are widely used in various areas of engineering. However, because of the complexity of actual objects, some difficulties still exist in the existing identification methods, and therefore further research is necessary.Gene Expression Programming (GEP) is a global optimization search technology developed in the past few years, and has been applied in many areas because of its powerful search capabilities and high evolution efficiency. In this paper, evolutionary nonlinear identification methods based on GEP are studied, and models'interpretability, simple practicality and intelligent identification progress are targets for research. The main content can be summarized as follows.1. The basic idea and the realizing framework of system identification using GEP is given, and the mixed GEP algorithm is also presented. Combining genetic algorithm, simulated annealing and PSO algorithm, a method of constant extraction and constant optimization from GEP expression is proposed. The mixed GEP identification algorithm for static nonlinear systems and time series prediction model is also studied. The experiment results illustrated the stability and superiority of the proposed algorithm.2. The drawbacks of the representation of dynamic system modeling using evolutionary algorithm are analyzed, and an especial method of dynamic items generation is proposed. Through introduction of variable terminals set, the algorithm can generate freely the necessary dynamic items of dynamic system. The simulation experiments illustrated that the variable terminals set has a higher performance.3. Based on the characteristics of NARMAX model and the multi-gene chromosome of GEP, a NARMAX model identification algorithm is proposed. The algorithm can give a more reasonable description of the model, and simplify the mapping mechanism between models and chromosomes.4. The coding schemes about Hammerstein model using GEP is given. Adding some transcendental functions in the terminals set, the algorithm expands the function expression of the Hammerstein model's nonlinear part, and can effectively reduce the number of the nonlinear items.5. The deficiency of the existing multiobjective system modeling algorithm is analyzed, and a more reasonable multiobjective evolutionary algorithm is proposed, and an implementation process about polynomial NARMAX model identification is given in detail. By defining the threshold of accuracy and the upper-limit value of complexity index, this algorithm can automatically maintain the number of effective solutions of the evolution population in an effective range through an automatic adjustment, and overcome the deficiency of early convergence of the original multi-objective optimization algorithm because of the superabundant effective solutions. The factors of models'accuracy and complexity are taken into account, and the algorithm can make the final solutions achieve a trade-off between the accuracy and the complexity.