CAJ | 학술논문

将差分进化算法用于非线性系统模型的参数估计,通过对谷氨酸菌体生长模型的参数估计进行验证,并与人工神经网络、遗传算法和微粒群算法的优化结果进行了比较.仿真试验结果表明:差分进化算法为非线性系统模型参数估计提供了一种有效的途径,对非线性系统模型的参数估计精度高于人工神经网络、遗传算法和微粒群算法.
장차분진화산법용우비선성계통모형적삼수고계,통과대곡안산균체생장모형적삼수고계진행험증,병여인공신경망락、유전산법화미립군산법적우화결과진행료비교.방진시험결과표명:차분진화산법위비선성계통모형삼수고계제공료일충유효적도경,대비선성계통모형적삼수고계정도고우인공신경망락、유전산법화미립군산법.
The differential evolution algorithm for nonlinear system model parameter estimation and validation of the glutamate cell growth model parameter estimates, and compared with the artificial neural networks, genetic algorithms and particle swarm algorithm optimization results. Simulation results show that: the differential evolution algorithm provides an effective way for nonlinear system model parameter estimation, the nonlinear system model parameter estimation accuracy than artificial neural networks, genetic algorithms and particle swarm optimization.