Airline Command: A Basic Decision Making Model:
'via Blog this'
Dec 19, 2011
Sep 22, 2011
It's Called a Systemigram
DM2 - The DoDAF Conceptual Data Model:
'via Blog this'
'via Blog this'
Browsing around, I found somebody drawing a systemigram, and not making any acknowledgements regrarding the name. I think they should be noticed.
Aug 28, 2011
Feb 7, 2011
Test Statistic?
A statistic is calculated from the sample. To begin with we assume that the hypothesis about the population parameter is true. We compare the value of the statistic with the hypothetical value of the parameter. If the difference between them is small, the hypothesis is accepted and if the difference between them is large, the hypothesis is rejected. A statistic on which the decision can be based whether to accept or reject a hypothesis is called test statistic. It is important to remember that a test-statistic does not prove the hypothesis to be correct but if furnishes as evidence against the hypothesis. Some of the test statistics to be discussed later are Z, t and Chi – Square.
Jan 4, 2011
Fuzzy Logic Explained
Today, I was reading a very short tutorial about Fuzzy Logic. It was particularly interesting because it was lecture notes from 1997, and despite the age, it was fresh and crisp and clear. Crisp, huh, what an interesting word to choose.
So the author explained fuzzy systems by a great example: lawn sprinkler system. I'm going to give you my 2 cent, quick summary of the paper.
1- you need two inputs. Example: a) outdoor air temperature b) soil moisture
2- Knowledge Base
a. Create membership function: For example. Cold is 30-47, Cool=40-70, normal=60-84,warm=75-94, hot=90-110
So one temperature membership function, and one moisture membership function. What's interesting here is that both these axis are calibrated to a y axis, called mu.
b. Make if then rules. The rule base needs to be verified by some sort of validated knowledge.
c. Fuzzification Module:
The name itself tells you alot about what is happeneing. Fuzzification of crisp inputs are made to fuzzy inputs. For example, outside temperature of 90 degrees would be transformed to 0.23 of "warm" in fuzzy terms.
d.Now it's time for decision making(rule strength). We use Min Max to make decisions.
example: if temp is hot (0.6) and soil is dry(0.27), then water duration is long. RS=0.27
e.Determine Fuzzy output: So if you have four outputs from D, then you go with majority. So if you have 2 long, and 2 mediums, your fuzzy output is long! The max of the rule strengths!
f. Lastly, it's time for Defuzzification: The purpose is to take the output of step e( fuzzy output), and come up with a crisp value. There are 4-5 methods to come up with a value, but in the lecture note, they use Center OF GRAVITY.
COG takes the area in abcdef, and finds the center of gravity. I'll hopefully have some time to add more ways to find the center of gravity.
The entire process is best captured in this diagram that I found on the internet.
So the author explained fuzzy systems by a great example: lawn sprinkler system. I'm going to give you my 2 cent, quick summary of the paper.
1- you need two inputs. Example: a) outdoor air temperature b) soil moisture
2- Knowledge Base
a. Create membership function: For example. Cold is 30-47, Cool=40-70, normal=60-84,warm=75-94, hot=90-110
So one temperature membership function, and one moisture membership function. What's interesting here is that both these axis are calibrated to a y axis, called mu.
b. Make if then rules. The rule base needs to be verified by some sort of validated knowledge.
The name itself tells you alot about what is happeneing. Fuzzification of crisp inputs are made to fuzzy inputs. For example, outside temperature of 90 degrees would be transformed to 0.23 of "warm" in fuzzy terms.
d.Now it's time for decision making(rule strength). We use Min Max to make decisions.
example: if temp is hot (0.6) and soil is dry(0.27), then water duration is long. RS=0.27
e.Determine Fuzzy output: So if you have four outputs from D, then you go with majority. So if you have 2 long, and 2 mediums, your fuzzy output is long! The max of the rule strengths!
f. Lastly, it's time for Defuzzification: The purpose is to take the output of step e( fuzzy output), and come up with a crisp value. There are 4-5 methods to come up with a value, but in the lecture note, they use Center OF GRAVITY.
COG takes the area in abcdef, and finds the center of gravity. I'll hopefully have some time to add more ways to find the center of gravity.
The entire process is best captured in this diagram that I found on the internet.
2011, and back up again
After some trouble with the blogger skin that disabled the contents to each post, I went to choose a blogger template that would no longer hassle or hinder the mission of this blog. Please check frequently for updates.
Vitech Core, a new practice to implement the DoDAF Feature
Based on my understanding of how Core Vitech works with DoDAF, I can see that the services are provided in the columbs in the left. This is an intelligent and functional way to approach DoDAF and the information that can be provided and sought after in DoDAF.
In Rational Rhapsody, erveything is captured in the model. So each model represents the activity. It’s more image based, but Core does not work on any images at all. It’s all about relationships and hierarchy. But that might be because that I’m looking in ER diagram.
This doesn't mean that one is better than other, i just think of it as another approach. Obviously I find Rational Rhapsody's approach more intuitive for the goals of DoDAF, but then Core does it differently. I need to spend more time and do more in analysis.
So Far, this is my candid assessment.
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