Deep learning-based parameter estimation of stochastic differential equations driven by fractional Brownian motions with measurement noise.
Jing FengXiaolong WangQi LiuYongge LiYong XuPublished in: Commun. Nonlinear Sci. Numer. Simul. (2023)
Keyphrases
- parameter estimation
- deep learning
- stochastic differential equations
- measurement noise
- brownian motion
- fractional brownian motion
- random fields
- differential equations
- stochastic processes
- stochastic process
- unsupervised learning
- diffusion process
- long range
- heavy traffic
- optimal control
- kalman filtering
- maximum likelihood
- poisson process
- model selection
- markov random field
- non stationary
- least squares
- em algorithm
- machine learning
- image sequences
- expectation maximization
- approximate inference
- optical flow
- three dimensional
- closed form solutions
- fractal dimension
- moving objects
- human motion
- kalman filter
- object tracking
- higher order
- dynamic programming
- video sequences