Rank: 8 based on 3718 downloads (last 30 days) and 66 files submitted
photo

Yi Cao

E-mail
Company/University
Cranfield University
Lat/Long
52.073917, -0.628756

Personal Profile:

http://www.cranfield.ac.uk/about/people-and-resources/academic-profiles/soe-ac-profile/dr-yi-y-cao.html


 

Watch this Author's files

 

Files Posted by Yi Cao View all
Updated   File Tags Downloads
(last 30 days)
Comments Rating
03 Apr 2014 Screenshot Pareto Set find the pareto set from n points with k objectives Author: Yi Cao optimization, multiobjective optimi..., pareto set, dorini 25 10
  • 3.66667
3.7 | 3 ratings
06 Feb 2014 Conjugate Gradient Method Conjugate Gradient Method to solve a system of linear equations Author: Yi Cao mathematics, linear algebra, linear equation, optimization 80 3
  • 5.0
5.0 | 2 ratings
12 Aug 2013 Screenshot Bivariant Kernel Density Estimation (V2.1) A tool for bivariant pdf, cdf and icdf estimation using Gaussian kernel function. Author: Yi Cao statistics, probability, bivariant gaussian ke..., kernel density estima..., bivariant pdf, cdf 78 8
  • 4.11111
4.1 | 9 ratings
11 Apr 2013 LAPJV - Jonker-Volgenant Algorithm for Linear Assignment Problem V3.0 A Matlab implementation of the Jonker-Volgenant algorithm solving LAPs. Author: Yi Cao linear assignment pro..., linear assignment pro..., optimization, hungarian algorithm, munkres algorithm 47 45
  • 4.875
4.9 | 16 ratings
19 Feb 2013 Screenshot Improvd downward branch and bound algorithm for regression variable selection Improved downward branch and bound to select the best subset for least squares regression problems. Author: Yi Cao optimization 13 0
Comments and Ratings by Yi Cao View all
Updated File Comments Rating
19 Apr 2014 Pareto Front Two efficient algorithms to find Pareto Front Author: Yi Cao

Adarsh,

If you delete the first line, you will get it working.

Good luck
Yi

01 Jan 2013 Fuel Cell Model Fuel Cell Model is presented Author: Siva Malla

The model has several "Bad Link", hence cannot run.

05 Sep 2012 Bidirectional Branch and Bound for Average Loss Minimization Two algorithms for selection of controlled variables using the average loss as the criterion. Author: Yi Cao

Hi Steffen,

If Y is rank difficient, the original formular has to change because it was derived based on the assuption YY^T is not signular. As you said, this only possiblelly happens when measurement errors are ignored. In other words, we can always add very small measurement errors to avoid such singularity. You can always assume Wn = eI with a sufficiently small e to make the code works.

Hope this helps.

Yi

06 May 2012 LAPJV - Jonker-Volgenant Algorithm for Linear Assignment Problem V3.0 A Matlab implementation of the Jonker-Volgenant algorithm solving LAPs. Author: Yi Cao

Thank you Dmitri, the bug has been fixed now.

Yi

21 Sep 2011 Hungarian Algorithm for Linear Assignment Problems (V2.3) An extremely fast implementation of the Hungarian algorithm on a native Matlab code. Author: Yi Cao

Well, I can see what you try to do is to increase the cost of selected assignment then to find next best assignment. However, you made a wrong change. The assignment results in dicated row 1 assigned with colume 3, but you miss understood as column 1 assigned with row 3. Wish this helps.

Comments and Ratings on Yi Cao's Files View all
Updated File Comment by Comments Rating
14 Dec 2014 Pareto Front Two efficient algorithms to find Pareto Front Author: Yi Cao Alex

Has anyone rewritten this into a pure MATLAB function?

28 Nov 2014 Learning the Unscented Kalman Filter An implementation of Unscented Kalman Filter for nonlinear state estimation. Author: Yi Cao Mark

@ Matthew (Jun 28)

I had the same problem (with P growing exponentially). Like you said: this has to do with the Alpha parameter. It has to do with how the Unscented Transform calculates its transformed mean. In certain cases (I think when measurement covariance is very low, and process covariance is a few orders of magnitude greater), there can be some rounding errors in Matlab, which causing the transformed mean to come up short.

To fix this, I changed the UT function to be like this:

~~~~~~~~~~

function [y,Y,P,Y1] = ut(f,X,Wm,Wc,n,R)

L=size(X,2);
% y=zeros(n,1); % LINE COMMENTED OUT HERE
Y=zeros(n,L);
for k=1:L
Y(:,k)=f(X(:,k));
% y=y+Wm(k)*Y(:,k); % LINE COMMENTED OUT HERE
end
y = mean([Y(:,1)'; mean(Y(:,2:end)')]); % LINE ADDED HERE
Y1=Y-y(:,ones(1,L));
P=Y1*diag(Wc)*Y1'+R;

24 Nov 2014 Efficient K-Means Clustering using JIT A simple but fast tool for K-means clustering Author: Yi Cao Junqi WANG

21 Nov 2014 Learning the Extended Kalman Filter An implementation of Extended Kalman Filter for nonlinear state estimation. Author: Yi Cao hua

I want to learn how to use EKF

16 Nov 2014 MPC Tutorial II: Multivariable and State Space MPC V2.0 A tool and tutorial for multivariable state space MPC Author: Yi Cao sam

.

Contact us