A nonlinear Kalman filtering approach to embedded control of turbocharged diesel engines

2014 ◽  
Author(s):  
Gerasimos Rigatos ◽  
Pierluigi Siano ◽  
Ivan Arsie
Keyword(s):  
Kalman Filtering ◽  
Diesel Engines ◽  
Embedded Control ◽  
Nonlinear Kalman Filtering ◽  
Filtering Approach

A Derivative-free Nonlinear Kalman Filtering Approach using Flat Inputs

2021 ◽  
pp. 1-25
Author(s):  
J. O. A. Limaverde Filho ◽  
E. L. F. Fortaleza ◽  
M. C. M. M. de Campos
Keyword(s):  
Kalman Filtering ◽  
Derivative Free ◽  
Nonlinear Kalman Filtering ◽  
Filtering Approach

scholarly journals Nonlinear Kalman Filtering in Affine Term Structure Models

2014 ◽  
Vol 60 (9) ◽  
pp. 2248-2268 ◽  
Author(s):  
Peter Christoffersen ◽  
Christian Dorion ◽  
Kris Jacobs ◽  
Lotfi Karoui
Keyword(s):  
Term Structure ◽  
Kalman Filtering ◽  
Affine Term Structure ◽  
Affine Term Structure Models ◽  
Term Structure Models ◽  
Nonlinear Kalman Filtering

scholarly journals High-Bandwidth Demodulation in MF-AFM: A Kalman Filtering Approach

2016 ◽  
Vol 21 (6) ◽  
pp. 2705-2715 ◽  
Author(s):  
Michael G. Ruppert ◽  
David M. Harcombe ◽  
S. O. Reza Moheimani
Keyword(s):  
Kalman Filtering ◽  
High Bandwidth ◽  
Filtering Approach

scholarly journals A Time-Varying Information Measure for Tracking Dynamics of Neural Codes in a Neural Ensemble

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 880
Author(s):  
Mohammad R. Rezaei ◽  
Milos R. Popovic ◽  
Milad Lankarany
Keyword(s):  
Kalman Filtering ◽  
Probability Distributions ◽  
Neural Code ◽  
Time Varying ◽  
Amount Of Information ◽  
Neural Ensemble ◽  
Different Types ◽  
Types Of Information ◽  
Different Time Scales ◽  
Filtering Approach

The amount of information that differentially correlated spikes in a neural ensemble carry is not the same; the information of different types of spikes is associated with different features of the stimulus. By calculating a neural ensemble’s information in response to a mixed stimulus comprising slow and fast signals, we show that the entropy of synchronous and asynchronous spikes are different, and their probability distributions are distinctively separable. We further show that these spikes carry a different amount of information. We propose a time-varying entropy (TVE) measure to track the dynamics of a neural code in an ensemble of neurons at each time bin. By applying the TVE to a multiplexed code, we show that synchronous and asynchronous spikes carry information in different time scales. Finally, a decoder based on the Kalman filtering approach is developed to reconstruct the stimulus from the spikes. We demonstrate that slow and fast features of the stimulus can be entirely reconstructed when this decoder is applied to asynchronous and synchronous spikes, respectively. The significance of this work is that the TVE can identify different types of information (for example, corresponding to synchronous and asynchronous spikes) that might simultaneously exist in a neural code.

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