Soft computing is a collection of artificial intelligence (AI) computing techniques that gives devices human-like problem-solving capabilities. It includes the basics of neural networks, fuzzy logic, and genetic algorithms.
The soft computing theory and techniques were introduced in the 1980s. And the term was coined by Lofti A. Zadeh, a mathematician, computer scientist, electrical engineer, AI researcher, and professor emeritus of computer science at the University of California, Berkeley.
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Unlike traditional computing (using physical data centers to store digital assets and run complete networks for daily operations), soft computing helps people solve more complex real-life problems.
And unlike hard computing, soft computing does not tolerate imprecisions, uncertainties, partial truths, and approximations. Soft computing is more accurate than any other kind of computing. It uses the human mind as a role model.
Techniques Used in Soft Computing
Various computing techniques come into play in soft computing, as shown below.
A concept also introduced by Zadeh (who coined the term “soft computing”), fuzzy logic is a computing approach that relies on “degrees of truth” rather than “true or false” (1 or 0) Boolean logic (which most computers use). It was introduced in the 1960s, two decades before soft computing.
To better understand what fuzzy logic is, take a look at the simple diagram below.
Genetic algorithms refer to a group of search methods that are inspired by the theory of evolution. As such, they create sets of solutions that evolve to get the lowest or highest value of an objective function or a linear expression (in math, that’s the formula you are given, such as f = c1x1 + … + cnxn).
They help obtain all the values that would possibly result given a specific objective function.
Artificial Neural Networks
An artificial neural network is a computer program that emulates a particular biological counterpart. A machine, therefore, designed to work as a human heart is an artificial neural network. It uses trial and error to get to the desired output.
At present, artificial neural networks are already used to spot fraudulent transactions, recognize people in photographs, predict outcomes, recognize speech and natural language, and more.
Machine learning (ML) is an AI branch that develops computers that learn and improve from experience without any human intervention or programming. Machines are fed tons of data, which they analyze for patterns and learn from using examples. Over time, they automatically make decisions and adjust actions accordingly.
An expert system is a computer or application that provides answers, solves complex problems, and makes decisions just like a human expert in a specialized domain. It performs complex calculations, analyses, and predictions and so is considered the first successful AI model.
Soft computing uses all the techniques mentioned above, among others, to come up with the most accurate solution to a complex human problem. But what is soft computing used for? Find out below.
Soft Computing Applications
Soft computing has a variety of real-world applications, including:
- It is widely used in games like poker and checker.
- Your everyday kitchen helpers (microwaves and rice cookers) make use of it as well.
- More than that, soft computing is also used in most home appliances, including washing machines, heaters, refrigerators, and air conditioners, among others.
- It is also used in robotics.
- Homes aren’t the only ones that benefit from soft computing. Offices and other businesses do, too. It is used in image processing and data compression.
- Law enforcement and the legal sector also benefit from it, as it is used for handwriting recognition.
As you’ve seen in this post, soft computing helps solve a lot of complex problems and, as such, figures in our daily lives even if we don’t know it.