fengtset tsai

 

Comments and Ratings by fengtset		View all 1 - 2 of 2
Updated	File	Comments	Rating
26 Nov 2008	Learning the Unscented Kalman Filter An implementation of Unscented Kalman Filter for nonlinear state estimation. Author: Yi Cao	The same question as Loki, if the measurement equation is nonlinear in state variable, the estimated state variable does not change with actural (simulated) state variable. Any suggestion?
26 Nov 2008	Learning the Unscented Kalman Filter An implementation of Unscented Kalman Filter for nonlinear state estimation. Author: Yi Cao	Hi, Dr. Cao, Is the covariance update correct? P=P1-K*P12'; %covariance update Some paper wrote: P=P1-K*P12*K'; fengtset

Files Matching fengtset's Watch List					View all 1 - 3 of 3
Updated		File	Tags	Downloads (last 30 days)	Comments	Rating
01 Jun 2011		Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian Author: John D'Errico		161	48	4.7619 4.8 | 21 ratings
07 Feb 2008		Neural Network training using the Unscented Kalman Filter A function using the unscented Kalman filter to train MLP neural networks Author: Yi Cao	unscented kalman filt..., parameter estimation, neural networks, fuzzy logic	31	2	5.0 5.0 | 2 ratings
31 Aug 2007		Unconstrained optimization using Powell Unconstrained optimization using Powell Author: Giovani Tonel	matlab, powell, unconstrained optimiz..., optimization	15	2	1.0 1.0 | 2 ratings

Comments and Ratings Matching fengtset's Watch List			1-5 of 56
Date	File	Comment by	Comments	Rating
20 Dec 2011	Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian	ehs		5
11 Jul 2011	Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian	Woodeast	Thanks a lot for this useful tool! I have a question about how far away will the program evaluate the fun from the initial point. I have a function which only exits in a certain range around the initial point(but big enough in my sense, e.g. starting at .1, requiring >0). However, the program will break down for evaluating out of range points after a fare amount of calculation. Could you please give me some idea about why this happens? (BTW, my function has some randomness, does this matter?) Thanks so much!	Comment only
26 Jun 2011	Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian	Andrew Newell	Excellent! Some of its predecessors also do accurate derivatives, but this one is particularly easy to use for gradients and Hessians.	5
28 Apr 2011	Unconstrained optimization using Powell Unconstrained optimization using Powell	Görög Márton		1
18 Apr 2011	Adaptive Robust Numerical Differentiation Numerical derivative of an analytically supplied function, also gradient, Jacobian & Hessian	Mark	H John, This looks great. I'm trying to use the Hessian function to find the Fisher Information Matrix for Max-likelihood. However the Hessian program is choosing parameters which are not allowable in my chosen model. How can I restrict the Hessian algorythmn for permissible parameter values	4