Nonlinear system identificationblack box modelingdeep learningDeep state space models (SSMs) are an actively researched model class for temporal models developed in the deep learning community which have a close connection to classic SSMs. The use of deep SSMs as a black-box identification model can...
In this paper, we propose a parameter estimation method for nonlinear state-space models based on the variational Bayes. It is proved that the variational posterior distribution of the hidden states is equivalent to a posterior distribution of the states of an augmented nonlinear state-space model....
This paper studies the pruned state-space system for higher-order perturbation approximations to DSGE models. We show the stability of the pruned approximation up to third order and provide closed-form expressions for first and second unconditional moments and impulse response functions. Our results in...
Linear Time Invariant (LTI) state space models are a linear representation of a dynamic system in either discrete or continuous time. Putting a model into state space form is the basis for many methods in process dynamics and control analysis. Below is the continuous time form of a model in...
The basic design tool in this nonlinear case is the choice of feedback control in order to make a "Lyapunov derivative negative". This idea needs to be completed for the robust design: it is necessary to construct a Lyapunov function for an uncertain nonlinear system and to obtain the ...
generateMATLABFunctionGenerate MATLAB functions that evaluate the state and output functions, and their Jacobians, of a nonlinear grey-box or neural state-space model(Since R2022b) idNeuralStateSpace/evaluateEvaluate a neural state-space system for a given set of state and input values and return...
We build the nonlinear state space model for analyzing the highly nonlinear system and then develop a Hammerstein-Wiener (H-W) model which consists of a static input nonlinear block with two-segment polynomial nonlinearities, a linear time-invariant dynamic block, and a static output nonlinear ...
NonlinearStateSpaceModel[{f,g},x,u] 表示模型 , . NonlinearStateSpaceModel[sys] 给出对应于系统模型 sys 的状态-空间表示. 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...
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 {...
A state-space model is commonly used for representing a linear time-invariant (LTI) system. It describes a system with a set of first-order differential or difference equations using inputs, outputs, and state variables. In the absence of these equations, a model of a desired order (or num...