Particle filters for state-space models with the presence of unknown static parameters

Particle Filters in State Space Models With the Presence of Unknown Static Parameters

Utilizing Kernel Adaptive Filters for Speech Enhancement within the ALE Framework

Performance of the linear models, widely used within the framework of adaptive line enhancement (ALE), deteriorates dramatically in the presence of non-Gaussian noises. On the other hand, adaptive implementation of nonlinear models, e.g. the Volterra filters, suffers from the severe problems of large number of parameters and slow convergence. Nonetheless, kernel methods are emerging solutions t...

Joint State and Parameter Estimation in Particle Filtering and Stochastic Optimization

Dynamic state-space models are useful for describing data in many different areas, such as engineering, finance mathematics, environmental data, and physical science. An important task when analyzing data by state-space models is estimation of the underlying state process based on measurements from the observation process. Bayesian filtering represents a solution of considerable importance for ...

Synchronization of a Heart Delay Model with Using CPSO Algorithm in Presence of Unknown Parameters

Heart chaotic system and the ability of particle swarm optimization (PSO) method motivated us to benefit the method of chaotic particle swarm optimization (CPSO) to synchronize the heart three-oscillator model. It can be a suitable algorithm for strengthening the controller in presence of unknown parameters. In this paper we apply adaptive control (AC) on heart delay model, also examine the sys...

Fitting Stochastic Volatility Models in the Presence of Irregular Sampling via Particle Methods and the Em Algorithm

Stochastic volatility (SV) models have become increasingly popular for explaining the behaviour of financial variables such as stock prices and exchange rates, and their popularity has resulted in several different proposed approaches to estimating the parameters of the model. An important feature of financial data, which is commonly ignored, is the occurrence of irregular sampling because of h...