Fuzzy logic and statistical probability is calculated pretty much same tool.
Where fuzzy logic describe the degree of truth and probability is the chance of happening some event. Is there any other key differences between them and please explain elaborately.
I read this topic for some time but don't get it properly.
Imagine looking at the sky and seeing only a few clouds. Is the weather good or bad? You are totally sure about the weather, but you can only say its somewhat good weather.
How about the weather next week? You can look at satellite images and try to predict the weather then; maybe you say "I'm rather certain that the weather will be good next week."
The first situation is caused by the fuzzy concept of good weather (it can be a little bit good). The second is probabilities (you belief it is going to be good weather).
Note that both concepts are easily confused, but they can also be mixed in a sensible way. You can have uncertainty (thus a probability distribution) over the degree of truth of a statement. This will then be a real-valued random variable, instead of the binary Bernouille variable you have for the case of uncertainty over a non-fuzzy statement.
80 percent good weather means the weather is somewhere between good and bad, but more towards the good end. For example some clouds on a otherwise blue sky. The statement The weather is good is only true to the degree 0.8.
You're sitting inside and have no window. You do not know how the sky looks like, but it's summer, and yesterday the weather was good. You expect the weather to be good with a chance of 80 percent.
Note that here, the weather is either good or bar, there is no in between. But you do not know which of the two possible states is the correct one.
You can also be unsure the current state of the weather, and in addition you want to have a fuzzy concept of good weather.