Fuzzy Logic and Probability : The Confusing Terms
Often through reading many articles and blogs on Data Science there are a few terms that we all come across and don’t actually get a clear understanding of the same. Moreover, by looking at the basic overview of the terms it often happens that in a few couples of days we come across the same terms again. So, it is necessary that we understand its detailed meaning so that we can hence apply it in the future in advance concepts as and when required. We are going to have a discussion on very popular terms today which often leads us in dilemma to understand the correct difference between the two. It is Fuzzy Logic and Probability. Classifying it as the same will lead to misconceptions in the future so let us understand the mentioned terms in detail.
“Fuzzy logic is a form of many-valued logic in which the truth value of variables may be any real number between 0 and 1 both inclusive.”
“Probability is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true.”
Both the terms are a way of expressing uncertainty but in a different way altogether. They possess a similar range interval between [0,1] though still having a minor difference on the same. It informs us that how uncertainty will change over a period of time when any kind of new data/information arrival takes place. Fuzzy Logic can be defined as a concept of partial truth. Probability can be seen as measures of the likelihood of a future occurrence based on something which has happened recently and is known very well. We can very well put forth that Probability is the subset of Fuzzy Logic.
Characteristics of Fuzzy Logic and Probability
|1.||Here, everything is a matter of degree.||It is specific within the range between 0 and 1.|
|2.||It is based upon natural language processing.||Not used for high approximations.|
|3.||The Best suitable for approximation use cases.||It captures partial knowledge.|
|4.||Readily integrable with programming.||Deals with likelihood.|
Used by Quantitative Analysts too for the improvisation
of their algorithms.
|Not capable of capturing any type of uncertainties.|
Difference between Fuzzy Logic and Probability:
|Sr. No.||Fuzzy Logic||Probability|
|1||Trying to understand the concept of vagueness.||
The main association is with events and to
Check whether the events will occur or not.
|2||This captures the meaning of partial truth.||This captures partial knowledge.|
|3||The degree of membership is in a set.||The probability event is in a set.|
Let us understand the 2 terms with a real-life example. Consider the following 2 examples:
Example 1: There is a chance of 95% rain this evening.
Example 2: It is going to rain very heavily in the evening today.
So here in the above examples, we can see that example 1 says that there is a very well chance of raining in the evening since it is strictly mentioned as 95% is the probability of the same. Whereas if we look through the second example, there is fuzziness in it, and we get to know that it is going to rain heavily, but it is not very clear how much heavy it is going to be. Here the ‘heavy’ term can be subjective depending upon the person interpreting it.
The following are some Industry oriented use cases of Fuzzy Logic:
1) Air conditioners
2) Facial Pattern Recognition
3) Vacuum Cleaners
4) Transmission Systems
5) Control of Subway systems
The following are some Industry oriented use cases of Probability:
1) Manufacturing Business
2) Decision-Making Process
3) Risk Evaluation
4) Scenario Analysis
5) Calculation of Long-term gains & losses
We have to make a note of one important minor difference in theoretical terms between fuzzy logic and probability. Fuzzy logic attaches a value between 0 and 1 which is uncertain and measures the degree to which the proposed statement is correct. In probability, it gives a value between 0 and 1, but it measures how likely is the proposed statement is correct. So while discussing fuzzy logic and probability it might seem that they are the same, yet they are having a difference.