Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters
Generalized Decoupled Polynomial Chaos for Nonlinear Circuits With Many Random Parameters
复制标题
具有许多随机参数的非线性电路的广义解耦多项式混沌
DOI：
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发表时间：
2015
影响因子：
3
通讯作者：
F. Canavero
中科院分区：
文献类型：
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作者：
P. Manfredi;D. Vande Ginste;D. De Zutter;F. Canavero
这封信提出了一种通用且有效的解耦技术，用于通过多项式混沌对非线性电路进行随机模拟。根据标准框架，随机电路波形仍然表示为正交多项式的展开式。然而，通过使用点匹配方法而不是传统的随机伽辽金方法，引入了一种变换，使多项式混沌系数解耦，因此可以通过重复的非侵入式模拟和逆线性变换来获得。正如整封信中所讨论的，所提出的技术克服了最先进方法的一些局限性。特别是，可扩展性得到了极大的提高，可以在多项式混沌框架内同时处理数十个随机参数。提供的验证应用示例涉及具有多达 25 个随机参数的微波放大器的统计分析。
This letter proposes a general and effective decoupled technique for the stochastic simulation of nonlinear circuits via polynomial chaos. According to the standard framework, stochastic circuit waveforms are still expressed as expansions of orthonormal polynomials. However, by using a point-matching approach instead of the traditional stochastic Galerkin method, a transformation is introduced that renders the polynomial chaos coefficients decoupled and therefore obtainable via repeated non-intrusive simulations and an inverse linear transformation. As discussed throughout the letter, the proposed technique overcomes several limitations of state-of-the-art methods. In particular, the scalability is hugely improved and tens of random parameters can be simultaneously treated within the polynomial chaos framework. Validating application examples are provided that concern the statistical analysis of microwave amplifiers with up to 25 random parameters.