NonlinearStateSpaceModel[eqns,{{x1,x10},…},{{u1,u10},…},{g1,…},t] 给出微分方程eqns的状态-空间模型,方程含有因变量xi,输入变量ui,工作值xi0和ui0,输出gi和自变量t. 更多信息和选项 范例 打开所有单元 基本范例(1) 定义非线性系统
文中使用了一个非线性非高斯分布的状态空间模型框架对配对交易进行建模。研究结果显示会比传统的配对模型有较大的改进。 核心模型 其中, P_A和P_B是两个资产的价格,而x是残差项,此处用了不同的模型来拟合残差项,包括f和g两个函数,最后一项g*mu,mu的概率分布假设是p,那么要拟合的参数模型就是f,g和p。其中...
Keywords: EM algorithm; exponential family; particle filters; sequential Monte Carlo methods; state space models; stochastic volatility model 1. Introduction In this paper, we study SMC methods for smoothing in nonlinear state space models. We consider a bivariate process (X, Y ), where X {...
Nonlinear State Space Modelsbest linear unbiased estimatorGaussian sum filtershidden Markov modelsKalman filternon‐Gaussian systemnonlinear state space modelsstate space modelsdoi:10.1002/9781119514312.ch7Ruey S. TayRong Chen
Tuning nonlinear state-space models using unconstrained multiple shooting A persisting challenge in nonlinear dynamical modelling is parameter inference from data. Provided that an appropriate model structure was selected, the id... J Decuyper,MC Runacres,J Schoukens,... - Ifac World Congress 被引...
The figure depicts a state space model for time-varying data. The emission and transition functions may be pre-specified to have a fixed functional form, a parametric functional form, a function parameterized by a deep neural networks or some combination thereof. Inference Model The box q(z1....
The figure depicts a state space model for time-varying data. The emission and transition functions may be pre-specified to have a fixed functional form, a parametric functional form, a function parameterized by a deep neural networks or some combination thereof. Inference Model The box q(z1....
The new results allow the application of these methods to state space models where the observation density p(yθ) is not log-concave. Additional results are presented that lead to computationally efficient implementations. We illustrate the methods for the stochastic volatility model with leverage. ...
A reduced order modeling (ROM) workflow, where you use deep learning to obtain a low-order nonlinear state-space model that serves as a surrogate for a high-fidelity battery model. The low-order model takes the current (charge or discharge) and state of charge (SOC) as inputs and predict...
This paper derives state space error bounds for the solutions of reduced systems constructed using proper orthogonal decomposition (POD) together with the discrete empirical interpolation method (DEIM) recently developed for nonlinear dynamical systems [SIAM J. Sci. Comput., 32 (2010), pp. 2737–276...