Three Improved Euler Methods for a Class of Simplified Quasi-cubics Function

Ya Wang, Yongguang Yu, Sha Wang
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Abstract

The simplified quasi-cubics function is used quite common in the ordinary differential equation, which is used to describe the biological neuron model. As complexity of the biological neuron model, its analytical solution hardly can be solved in common situation. Numerical solution of the model is very important in reality. Three fast convergence methods are proposed for the simplified function by means of changing the step size of the classical Euler method in this manuscript. The results obtained are pretty good. First method is to correct the step size of two directions. Second method is to correct and encrypt two directions' step size. And the last method is to search unidirectional step size. On one hand, the feasibility of the three methods are proved based on the theoretical analysis. On the other hand, numerical results and comparison show that the advised three methods are effective to get the numerical solution with small population size, enhanced convergence speedup and improved precision.
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一类简化拟三次函数的三种改进欧拉方法
简化拟三次函数是常微分方程中常用的描述生物神经元模型的函数。由于生物神经元模型的复杂性,其解析解在一般情况下很难求解。该模型的数值解在现实中具有十分重要的意义。本文通过改变经典欧拉方法的步长,提出了简化函数的三种快速收敛方法。所得结果相当不错。第一种方法是对两个方向的步长进行校正。第二种方法是对两个方向的步长进行校正和加密。最后一种方法是单向搜索步长。一方面,在理论分析的基础上,论证了三种方法的可行性。另一方面,数值结果和比较表明,所建议的三种方法可以有效地获得较小种群规模的数值解,增强了收敛速度,提高了精度。
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