研究基于分数阶黏弹性材料构造的Van der pol减振系统在外部宽带噪声激励下的随机稳定性和随机分岔行为.考虑约束条件的影响,引入非平滑Zhuravlev变换,将碰撞系统转化为无碰撞的动力学系统.利用一组拟周期函数近似替换分数阶微分,通过...研究基于分数阶黏弹性材料构造的Van der pol减振系统在外部宽带噪声激励下的随机稳定性和随机分岔行为.考虑约束条件的影响,引入非平滑Zhuravlev变换,将碰撞系统转化为无碰撞的动力学系统.利用一组拟周期函数近似替换分数阶微分,通过随机平均法得到系统的It8随机微分方程,根据最大Lyapunov指数法和奇异边界理论分类讨论系统的随机稳定性,利用拟Hamilton系统随机平均法分析系统在线性It8方程下的随机分岔行为,得到D-分岔的临界条件,进一步求出与系统幅值相关的稳态概率密度函数.使用MATLAB绘制稳态概率密度曲线,直观展现系统发生的稳态变化.结果表明,当分数阶阶次和噪声强度在一定阈值内变化时,可诱导系统产生P-分岔行为.展开更多
Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stocha...Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.展开更多
文摘研究基于分数阶黏弹性材料构造的Van der pol减振系统在外部宽带噪声激励下的随机稳定性和随机分岔行为.考虑约束条件的影响,引入非平滑Zhuravlev变换,将碰撞系统转化为无碰撞的动力学系统.利用一组拟周期函数近似替换分数阶微分,通过随机平均法得到系统的It8随机微分方程,根据最大Lyapunov指数法和奇异边界理论分类讨论系统的随机稳定性,利用拟Hamilton系统随机平均法分析系统在线性It8方程下的随机分岔行为,得到D-分岔的临界条件,进一步求出与系统幅值相关的稳态概率密度函数.使用MATLAB绘制稳态概率密度曲线,直观展现系统发生的稳态变化.结果表明,当分数阶阶次和噪声强度在一定阈值内变化时,可诱导系统产生P-分岔行为.
文摘Proximal gradient descent and its accelerated version are resultful methods for solving the sum of smooth and non-smooth problems. When the smooth function can be represented as a sum of multiple functions, the stochastic proximal gradient method performs well. However, research on its accelerated version remains unclear. This paper proposes a proximal stochastic accelerated gradient (PSAG) method to address problems involving a combination of smooth and non-smooth components, where the smooth part corresponds to the average of multiple block sums. Simultaneously, most of convergence analyses hold in expectation. To this end, under some mind conditions, we present an almost sure convergence of unbiased gradient estimation in the non-smooth setting. Moreover, we establish that the minimum of the squared gradient mapping norm arbitrarily converges to zero with probability one.
