What is soft computing? It is a question asked by many as it is the new kid on the block and many do not know much about it. Yet, it is one of the front running technologies which is defining the future of computing.
We often come across problems wherein we do not have precise information to solve the problem.
There might be uncertainty in such cases regarding the input we have. Such problems cannot be solved by traditional problem-solving methods.
This is where soft-computing comes into the picture. There are many definitions out there that are highly technical and can be a little difficult to understand, so let’s dwell on the topic and understand it better.
What is Soft Computing?
In computer programming, every action is based on a certain yes or no logic.
The tasks are then performed accordingly based on the signal received. Whenever algorithms are designed, the same logic is applied, what if the answer is yes, and what if the answer is no.
And in all such cases, there is always a definite answer, which is the only correct answer and there is no other possible correct answer.
Now consider this scenario and think, there are certain scenarios which do not have exact and precise parameters.
How do you compute results for them? This is something that our computers are not equipped for, but our human brain is.
Now consider this ability is being provided to the computers as well – think the way our human brain does.
With soft computing, this is exactly what is being materialized. The applications for soft computing are galore and there is no dearth as to where it could be useful.
But the crux is, this is the new age of computing and it is revolutionizing the way computing is performed and is pushing the scope of computer applications.
There are various computational methods or technologies that come under the umbrella of soft-computing. Some of them are as follows:
- Evolutionary computing
- Artificial neural networks
- Probabilistic computing
- Causal models
- Case-based reasoning
- Fuzzy logic
- Interactive computational models
Applications of Soft Computing in Different Industries
Soft computing has already found applications in numerous industries, both business and consumer-centric types. Some of the applications are covered below:
Communication requires a very dynamic environment as the demand can occur randomly, and most of the time, there is not much of a pattern to work with.
Soft computing uses an artificial neural network and fuzzy logic to determine when there is a sudden surge in demand and accordingly allocates resources for that particular node.
Not only does it help save cost through reduced usage, but also helps save substantial resources that can be diverted to other areas that currently demand higher bandwidth.
This is a very interesting application since we are already using some of this.
Our everyday appliances such as refrigerators, microwaves, washing machines, etc. are becoming smart because of artificial intelligence, machine learning, and fuzzy logic.
Not only can they communicate with the users about their usage, but they can also adjust their settings according to the workload at that very moment.
This has helped them be very efficient in their operations and also help the consumers save a substantial amount over time.
This is one of the very upcoming fields to use soft computing’s fuzzy logic and expert systems techniques.
It helps manage industries efficiently in not only production but also inventory management.
Some of the large e-commerce companies are already employing robots with soft computing embedded to help manage the substantial load of goods that goes through the warehouse on a daily basis.
With connected cars, transportation is another major industry making use of soft computing at its various stages.
Right from the production of cars in the factory to be on the road for navigation, traffic prediction, troubleshooting, and diagnostics of the car, fuzzy logic and evolutionary computing are widely used.
Similar solutions are also used in elevators when a single system is in charge of handling multiple elevators.
No one would have guessed this as an application since it’s such a critical industry.
One wrong decision can result in loss of lives or permanent damage to the patients.
But soft computing, using a horde of its various logic has been found to be quite accurate in terms of diagnosis and results.
Doctors are increasingly turning towards soft computing to diagnose the patients’ ailments from the symptoms accurately and hence save on money and side effects from medications of the wrong diagnosis.
Moreover, early diagnosis of critical diseases has also helped save numerous live which otherwise would have been lost.
Conclusion
We have seen above as to the number of applications in which soft computing performs exceptionally.
It is interesting to note that a machine can now think and function similar to humans, which can also have a flipside to the entire scenario that machines could replace humans at their workplace.
But it is not yet a foolproof system and given the numerous circumstances and conditions soft computing has to endure, it is a fact that it can never fully replace a human mind when it comes to making critical decisions.
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