Results 1 - 10 of 156

Mixture Kalman filters

by Rong Chen, Jun S. Liu , 2000
"... In treating dynamic systems,sequential Monte Carlo methods use discrete samples to represent a complicated probability distribution and use rejection sampling, importance sampling and weighted resampling to complete the on-line `filtering' task. We propose a special sequential Monte Carlo metho ..."
Abstract - Cited by 224 (8 self) - Add to MetaCart
method,the mixture Kalman filter, which uses a random mixture of the Gaussian distributions to approximate a target distribution. It is designed for on-line estimation and prediction of conditional and partial conditional dynamic linear models,which are themselves a class of widely used non-linear

Switching Kalman Filters

by Kevin P. Murphy , 1998
"... We show how many different variants of Switching Kalman Filter models can be represented in a unified way, leading to a single, general-purpose inference algorithm. We then show how to find approximate Maximum Likelihood Estimates of the parameters using the EM algorithm, extending previous results ..."
Abstract - Cited by 67 (2 self) - Add to MetaCart
on learning using EM in the non-switching case [DRO93, GH96a] and in the switching, but fully observed, case [Ham90]. 1 Introduction Dynamical systems are often assumed to be linear and subject to Gaussian noise. This model, called the Linear Dynamical System (LDS) model, can be defined as x t = A t x t

Exponential Hebbian On-Line Learning Implemented in FPGAs

by M. Rossmann, T. Jost, K. Goser, A. Bühlmeier, G. Manteuffel - Marlsburg, C., von Seelen, W., Vorbrggen, J.C. & Sendhoff, B. (eds) Artificial Neural Networks - ICANN 96 , 1996
"... . Hebbian learning is a local learning algorithm and allows an on-line adaptation of the weights. Therefore an artificial neural network with built-in hebbian learning is capable of learning on operation. This paper presents the implementation of this algorithm in a digital Field Programmable Gate A ..."
Abstract - Cited by 3 (3 self) - Add to MetaCart
synapses in a single FPGA device gets possible. The neuron comprises four conventional synapses with fixed weights and four hebbian synapses for exponential on-line learning. Experiments show the improved performance of this system compared with a linear solution. 1 Introduction Hebbian learning

The Square-Root Unscented Kalman Filter for State and Parameter-Estimation

by Rudolph Van Der Merwe, Eric A. Wan - in International Conference on Acoustics, Speech, and Signal Processing , 2001
"... Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e.g., ..."
Abstract - Cited by 101 (8 self) - Add to MetaCart
Over the last 20-30 years, the extended Kalman filter (EKF) has become the algorithm of choice in numerous nonlinear estimation and machine learning applications. These include estimating the state of a nonlinear dynamic system as well estimating parameters for nonlinear system identification (e

On-line Identification of Non-Linear Systems Using an Adaptive RBF-Based Neural Network

by Mohammad Reza Jafari, Tohid Alizadeh, Mehdi Gholami, Abdollah Alizadeh, Karim Salahshoor
"... Abstract — This paper extends the sequential growing and pruning radial basis function (GAP-RBF) to cater for on-line identification of non-linear systems. Some desired modifications on the growing and pruning neurons criteria have been proposed for the original GAP-RBF to make it more suitable for ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
for on-line identification. The unscented kalman filter (UKF) has been proposed as a new learning algorithm for GAP-RBF neural network. Moreover, a variable forgetting factor strategy has been included in the UKF algorithm to keep the parameter estimation routine more active in time-varying dynamics

The Kalman Filter Explained

by Tristan Fletcher , 2010
"... The aim of this document is to derive the filtering equations for the simplest Linear Dynamical System case, the Kalman Filter, outline the filter’s implementation, do a similar thing for the smoothing equations and conclude with parameter learning in an LDS (calibrating the Kalman Filter). ..."
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The aim of this document is to derive the filtering equations for the simplest Linear Dynamical System case, the Kalman Filter, outline the filter’s implementation, do a similar thing for the smoothing equations and conclude with parameter learning in an LDS (calibrating the Kalman Filter).

Robust on-line beat tracking with kalman filtering and probabilistic data association (kf-pda

by Yu Shiu, Student Member, Namgook Cho, Student Member, Pei-chen Chang, C. -c. Jay Kuo - IEEE Transactions on Consumer Electronics , 2008
"... Abstract — A Kalman filtering (KF) approach to on-line musical beat tracking with probabilistic data association (PDA) is investigated in this work. We first formulate the beat tracking process as a linear dynamic system of beat progression, and then apply the Kalman filtering algorithm to the dynam ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
Abstract — A Kalman filtering (KF) approach to on-line musical beat tracking with probabilistic data association (PDA) is investigated in this work. We first formulate the beat tracking process as a linear dynamic system of beat progression, and then apply the Kalman filtering algorithm

Smart Archive For On-Line Learning Systems

by Kauko Väinämö, Juha Röning, Perttu Laurinen
"... The amount of information grows at an exponential speed, posing increasing demands for the systems processing that information. The focus in future intelligent systems will be to find and store the information significant for the overall system. This involves data warehousing or data mining techniqu ..."
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techniques as well as machine learning. The meaningful information affects the ability of systems to learn and adapt to the ever-changing reality. In this paper, we present a concept of a smart archive, to be used as a memory and information processor for on-line learning systems. The overall concept

Nonlinear Time Series Filtering, Smoothing and Learning using the Kernel Kalman Filter

by Liva Ralaivola
"... In this paper, we propose a new model, the Kernel Kalman Filter, to perform various nonlinear time series processing. This model is based on the use of Mercer kernel functions in the framework of the Kalman Filter or Linear Dynamical Systems. Thanks to the kernel trick, all the equations involved in ..."
Abstract - Cited by 7 (0 self) - Add to MetaCart
In this paper, we propose a new model, the Kernel Kalman Filter, to perform various nonlinear time series processing. This model is based on the use of Mercer kernel functions in the framework of the Kalman Filter or Linear Dynamical Systems. Thanks to the kernel trick, all the equations involved

On-line Mobile Robotic Dynamic Modeling using Integrated Perturbative Dynamics

by Forrest Rogers-marcovitz, Mihail Pivtoraiko , 2010
"... Mobile robotic dynamics modeling is necessary for reliable planning and control of unmanned ground vehicles on rough terrain. Autonomous vehicle research has continuously demonstrated that a platform’s precise understanding of its own mobility is a key ingredient of competent machines with high perf ..."
Abstract - Cited by 2 (2 self) - Add to MetaCart
. The slip rates are first learned for steady state conditions and interpolated to slip rate surfaces. An Extended Kalman Filter uses the residual pose differences for on-line identification of the perturbative parameters on a six wheel, skid steered vehicle. An order of magnitude change in relative pose
Results 1 - 10 of 156