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What is Soft Computing?

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  • Written By: Ken Black
  • Edited By: Andrew Jones
  • Last Modified Date: 19 September 2016
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Traditional electronic computations tend to be black and white. When working in binary code, with sequences of zeros and ones, there is no chance for anything else but simple "yes or no" answers. While that may be an adequate way of computing for many tasks, soft computing takes a different approach. In short, soft computing allows the computer to take on a certain level of imprecision in its work. Some may equate this with artificial intelligence, in that it is similar to the way the human brain works.

From a human perspective, soft computing introduces compromises into a computer's processing, that are not present in hard computing. There are times, when the answer to a question may be yes or no, but there is not yet enough information to calculate definitively what the answer is. Traditional computers facing this situation will simply stop and wait until there is enough information to draw a precise conclusion. Soft computing is, in essence, the capability of a computer to provide an answer of maybe, or even to make an educated guess as to what the answer may be until more information becomes available.

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To use a mathematical example, it is simple to say the sum of two plus two is four. It is also correct to say the sum of two plus two is somewhere between three and five. Of course, the object is to come up with the most precise answer possible. While a computer may be tempted to disregard the second option, soft computing, if done properly, will see this answer as an potential option. While the computer will still always opt for the most precise answer available, it will consider making an estimate, if not all of the numbers are known for certain.

To come up with its answers, or its assessment of the answers, the computer will use many different disciplines. Among the five most well known are "fuzzy" systems, evolutionary computation, probabilistic reasoning, machine learning, and neural networks. By using many different computational methods for analyzing a problem, the computer may eventually come up with a precise answer to a question that had an imprecise answer initially.

In effect, the computer has come up with an answer that was not pre-programmed into it. From a computer science perspective, and possibly from a biological perspective, this could be considered learning, or artificial intelligence. Some may argue the path to the answer was pre-programmed, whether the answer was or not, thus not constituting real intelligence. The question of whether this constitutes actual intelligence is a philosophical matter, which likely greatly depends on one's own perspective.

The field of computer science is generally excited about the possibility of soft computing and its potential benefits. It could revolutionize robotics, perhaps making more life-like prosthetics that are easier to use, and which move more naturally. Soft computing could also be used in many other fields, such as medicine, engineering and physics.

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