大数据混沌模型寻优计算过程数学建模仿真

Mathematical Modeling Simulation Based on Big Data Chaos Optimization Calculation Process

针对大数据混沌模型寻优计算过程无法在全局范围内实现最优并且具有不确定性因素的情况,提出一种基于改进遗传算法的大数据混沌模型寻优计算方法。对关联维数、最大Lyapunov指数以及时间序列熵进行分析和提取,依据获取的结果,采用实数编码对原参数进行遗传操作,将误差绝对值时间积分性能指标当做参数选择的最小目标函数,在目标函数中引入控制输入的平方项和惩罚函数,求出最优指标与适应度函数,通过适应度比例法和最优保留策略完成选择操作,通过选择、交叉、变异算子对种群进行处理,产生下一代种群,直至参数收敛或达到要求。仿真实验结果表明,所提方法具有很高的寻优能力。

in view of the large data model chaos optimization calculation process is easy to be in the global scope to be opti-mal, and has a lot of uncertainty factors, based on improved genetic algorithm is a kind of calculation method of the large da-ta chaos optimization model, the correlation dimension and maximum Lyapunov index and entropy of time series analysisand extraction, according to the obtained results, the real-coded genetic operation on the original parameters, the time inte-gral of absolute value of error performance index as the minimum objective function of parameter choice, the introduction ofthe square of control input items in the objective function and penalty function, find out the optimal index and the fitnessfunction, through the fitness proportion method and completion of the elitist reserve strategy selection operation, throughthe selection, crossover and mutation operator to deal with population, produce the next generation of population, until theparameter convergence or meet the requirements.The simulation results show that the proposed method has high optimiza-tion ability.