Sökning: "Nonlinear filters"
Visar resultat 1 - 5 av 48 avhandlingar innehållade orden Nonlinear filters.
1. System Modeling Using Basis Functions and Application to Echo Cancelation
Sammanfattning : This thesis concerns modeling of linear and nonlinear dynamic systems, where the applied models can be described by basis function expansions. In the first part of the thesis, the investigated model (or filter) structures and parameter estimation algorithms are described. LÄS MER
2. Identification of Stochastic Nonlinear Dynamical Models Using Estimating Functions
Sammanfattning : Data-driven modeling of stochastic nonlinear systems is recognized as a very challenging problem, even when reduced to a parameter estimation problem. A main difficulty is the intractability of the likelihood function, which renders favored estimation methods, such as the maximum likelihood method, analytically intractable. LÄS MER
3. Generation and Applications of Picosecond Pulses in High-Capacity Fiber Optic Systems
Sammanfattning : This thesis deals with short optical pulse generation from actively mode-locked Erbium-doped fiber ring lasers (ML-EFRL) and their applications in all-optical signal processing (optical sampling and switching) and transmission experiments for high-capacity fiber optic systems. In future ultra-high bit-rate optical time division multiplexed (OTDM) systems, one of the key requirements is to develop reliable optical pulse sources that can generate high quality optical pulses. LÄS MER
4. Kalman Filters for Nonlinear Systems and Heavy-Tailed Noise
Sammanfattning : This thesis is on filtering in state space models. First, we examine approximate Kalman filters for nonlinear systems, where the optimal Bayesian filtering recursions cannot be solved exactly. These algorithms rely on the computation of certain expected values. LÄS MER
5. Particle filters and Markov chains for learning of dynamical systems
Sammanfattning : Sequential Monte Carlo (SMC) and Markov chain Monte Carlo (MCMC) methods provide computational tools for systematic inference and learning in complex dynamical systems, such as nonlinear and non-Gaussian state-space models. This thesis builds upon several methodological advances within these classes of Monte Carlo methods. LÄS MER