## Questions Exercise 1

Moderator: Lernende Roboter

DreamFlasher
BASIC-Programmierer
Beiträge: 102
Registriert: 12. Okt 2010 12:44

### Questions Exercise 1

Hi,

we should start using the forum for some questions of generel interest
So our questions to exercise 1:
- 1.3: Why isn't the acceleration force for component q1: $$(m1+m2)*\ddot{q_1}$$ as both masses affect joint 1?

...TBC...
Marcel Ackermann
http://www.dreamflasher.de
Machine Learning, Natural Language Processing, Algorithms

Interesse an Machine Learning, Artificial Intelligence, Natural Language Processing? Du möchtest deine Skills und Wissen verbessern, an Wettbewerben mit anderen Begeisterten teilnehmen? Mach mit bei unserer Study Group: http://groups.google.com/group/ml-ai

franzose
BASIC-Programmierer
Beiträge: 146
Registriert: 9. Okt 2009 00:08

### Re: Questions Exercise 1

Please apologize if I haven't understood your question, but actually I think that in the solution the mass matrix for $$u_1$$ exactly does what you suggested (at least for the part that is dependent from $$\ddot{q_1}$$):

You have $$M(q) * \ddot{q}$$ so you have to multiply the matrix with the vector $$(\ddot{q_1}, \ddot{q_2})^T$$ and in the first row and first col of the matrix there is $$m_1 + m_2$$.

This results to $$u_1 = (m_1 + m_2) * \ddot{q_1} + k * \ddot{q_2}$$ with $$k$$ being the term in the first row and second col of the mass matrix.
Zuletzt geändert von franzose am 27. Nov 2012 00:05, insgesamt 1-mal geändert.

lustiz
Mausschubser
Beiträge: 70
Registriert: 29. Apr 2009 10:28

### Re: Questions Exercise 1

Another question for you guys, concerning exercise 1.2 b) (Linear Least Squares):

Are we supposed to include a bias term? As far as I know you usually include a bias yet if we talk about 2 features, does that mean
a) [ sin(x); sin(2x) ] or
b) [ 1; sin(x); sin(2x) ] ?

Cheers!

lustiz
Mausschubser
Beiträge: 70
Registriert: 29. Apr 2009 10:28

### Re: Questions Exercise 1

Also: Is the kernel function right in 1.2 (f) ?
I get pretty good results when I use
$$k(x_i, x_j) = e ^ \frac{- 0.5 || x_i - x_j ||^2}{\sigma^2}$$

However, there is no hope when I use the proposed one:
$$k(x_i, x_j) = e ^ \frac{|| x_i - x_j ||^2}{\sigma^2}$$

Any suggestions?

eesti
BASIC-Programmierer
Beiträge: 116
Registriert: 6. Okt 2008 20:59
$$k(x_i, x_j) =e^\frac{-\|x_i−x_j\|^2}{\sigma^2}$$