System Identification for State-space Models Based on the Variational Bayes Method
نویسندگان
چکیده
منابع مشابه
Variational Bayes for Continuous-Time Nonlinear State-Space Models
We present an extension of the variational Bayesian nonlinear state-space model introduced by Valpola and Karhunen in 2002 [1] for continuous-time models. The model is based on using multilayer perceptron (MLP) networks to model the nonlinearities. Moving to continuous-time requires solving a stochastic differential equation (SDE) to evaluate the predictive distribution of the states, but other...
متن کاملSystem Identification Based on Online Variational Bayes Method and Its Application to Reinforcement Learning
In this article, we present an on-line variational Bayes (VB) method for the identification of linear state space models. The learning algorithm is implemented as alternate maximization of an on-line free energy, which can be used for determining the dimension of the internal state. We also propose a reinforcement learning (RL) method using this system identification method. Our RL method is ap...
متن کاملVariational Bayes Inference for Logic-Based Probabilistic Models on BDDs
Statistical abduction is an attempt to define a probability distribution over explanations derived by abduction and to evaluate them using their probabilities. In statistical abduction, deterministic knowledge like rules and facts are described as logic formulas. However nondeterministic knowledge like preference and frequency seems difficult to represent as logic. Bayesian inference can reflec...
متن کاملVariational Learning for Switching State-Space Models
We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learnsthe parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models -- hidden Markov models and linear dynamical systems -- and is closely related to models that are widely used in ...
متن کاملSystem identification of nonlinear state-space models
This paper is concerned with the parameter estimation of a general class of nonlinear dynamic systems in state-space form. More specifically, a Maximum Likelihood (ML) framework is employed and an Expectation Maximisation (EM) algorithm is derived to compute these ML estimates. The Expectation (E) step involves solving a nonlinear state estimation problem, where the smoothed estimates of the st...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Transactions of the Society of Instrument and Control Engineers
سال: 2012
ISSN: 0453-4654,1883-8189
DOI: 10.9746/sicetr.48.102