How do I determine shaded cells in K-tables for minimizing multiple outputs?

In summary, the conversation discusses using K-tables to minimize functions (sum of minterms) and finding essential primary implicants. The procedure involves identifying shaded cells in the K-tables which correspond to the essential primary implicants. The question asks for clarification on how to find these shaded cells using K-tables, as well as resources for understanding the method.
  • #1
Korisnik
62
1

Homework Statement


http://i.imgur.com/VYPECuW.png?1 (f1: f2: f3: f1*f2: f1*f3: f2*f3: f1*f2*f3: is what's written near tables)
I need to minimize the functions (sum of minterms: f1 0, 1, 2, 4, 5, 11, 15; f2 0, 2, 4, 13, 15; f3 0, 1, 3, 4, 5, 7, 13, 15) using K-tables... This is what they show as procedure (they write after the picture:
f1 = c+d+f
f2 = b+c+e
f3 = g+b/d + e/h
and so on...

I see those (c, d, f, b, e, g, h) are the (essential) primary implicants in which there are shaded cells. I cannot determine how to find these shaded cells! I can't find the rule they used to determine them... I see that they must be in essential prim. implicants, but if I added another rule: for example they must not be in the f1*f2*f3, then why isn't also minterm[2] in f2 shaded? I've found some tutorials on the internet but they don't use these steps...

Thx in advance!
(just to make it clear, i don't need a full solution, only how to find these shaded cells ~ what do they mean)
 
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  • #2
Could someone help? I know how to do it using Quine McCluskey, I just can't understand what happened in that step; what's the method of solving these using k maps. Links, tutorial, anything?
 
  • #3
Is the question unclear? Does nobody know how to minimize this?
 

1. What is multiple output minimization?

Multiple output minimization is a method used in scientific research to optimize systems that involve multiple output variables. This method aims to find the best combination of input parameters that will result in the minimum output for all variables simultaneously.

2. Why is multiple output minimization important in scientific research?

Multiple output minimization is important because it allows scientists to efficiently optimize complex systems with multiple variables. By finding the best combination of input parameters, scientists can save time and resources in their research and improve the overall performance of their systems.

3. What are the benefits of using multiple output minimization?

The main benefits of using multiple output minimization include improved system performance, reduced time and cost in research, and the ability to account for the interdependence of multiple output variables. This method also allows for a more comprehensive analysis of a system and can help identify potential trade-offs between different variables.

4. What are some common techniques used in multiple output minimization?

Some common techniques used in multiple output minimization include gradient descent, genetic algorithms, and particle swarm optimization. These methods involve finding the optimal solution by iteratively adjusting the input parameters and evaluating their impact on the multiple output variables.

5. How can multiple output minimization be applied in different fields of science?

Multiple output minimization can be applied in various fields of science, including engineering, biology, environmental science, and economics. It can be used to optimize the performance of complex systems such as chemical processes, ecological systems, and economic models, among others.

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