Fuzzy Inference System Walkthrough | Fuzzy Logic, Part 2

Video quality	The size	Download

Information Fuzzy Inference System Walkthrough | Fuzzy Logic, Part 2

Title	:	Fuzzy Inference System Walkthrough \| Fuzzy Logic, Part 2
Lasting	:	16.13
Date of publication	:
Views	:	80 rb

How did the 4th rule get added to the 3rd rule to produce the new value of High? Did you connect them by OR?
Comment from : Floran Kacaku

Fantastic content - thank's a lot!
Comment from : л

How to find rules on the basis of clusters using fuzzy c means brPlease explain
Comment from : Mamta Gupta

I wish you include MATLAB example file with this excellent illustration
Comment from : amjad ali

Very good illustration level My special thanks ❤
Comment from : amjad ali

Fuzzyfierr : V1 > degree of membership 1brrule based inference : Degree of membership v 1 > Degree of membership v2brDefuzzifier :Degree of membership 2 > v 2
Comment from : Ahmed Y M Daraghma

Thanks for the great explanationbrbrIs it possible to get the link for the application you used in the video?
Comment from : yamen malik

Hello, thank you, very clear explanation I just have a question:brThe centroid we get at defuzzification step, is that in fuzzified values for the output variable (0-1), or crisps value? Because when you plot 3d surface, all seem to be in crisp value? Thanks
Comment from : hung bui

hi Sir , I hope that ur fine i have question brwhat 's the diffirent if we use a TRAPEZOID fonction or GAUSSIENNE Fonction or TRIANGLE fonction?
Comment from : FitnessSHORTS

great!
Comment from : Ricardo Barros

absoultely amazing
Comment from : rajeev kumar

I will reassure everyone that if the food is dog and the service is dog, the tip will be a nice round 0
Comment from : Frostbite

crazy! tks a lot Brian, u made it simple and understandable! hat off!
Comment from : Đức Hùng Nguyễn

how to change the scale of y-axis in Fuzzy logic Designer?
Comment from : Mehwish Ali Naqvi

great explanation thanks
Comment from : Hafiezamani Ahmad

wow, such a great explaining, thank you, one question please, I just couldn't understand why you chose a specific shape of membership for each one of the inputs? why couldn't all be gaussian for example? thank you again
Comment from : Shwan Agha

Wonderful job and just great Thank you very much
Comment from : ISHARA UDAYANGA Wimalaweera

Great Video!! I don't known the fuzzy analysis and the video it was very clear I will try this kind of solution in some real life situation
Comment from : Paolo

Thank you for the amazing video, it was really helpful
Comment from : Muhammad Ragab

This type of work is the one that deserves support Just to show some appreciation
Comment from : Hadhlul Aladhyani

Thank you a lot! Your videos are amazing!
Comment from : irini provatidou

Master piece class thanks
Comment from : surflaweb

It's a great explaination
Comment from : Tống Văn Ngọc_GV-ĐĐT

Is it possible to create the model using the neuro fuzzy designer but deploy the model to html
Comment from : Jovelyn Obias

Excellent illustrations !
Comment from : Stefan Brock

You rock man:) Keep it up
Comment from : mohamad alikhani

very very cool You are an amazing teacher brian, thank you
Comment from : Ryan Foss

when is the next part expected?
Comment from : Nejlontacskó

This is great and all, but I for one already interacted with making an FIS by using it with the tip example Would like to see real applications of FIS with AI somewhere, lol Though I will say that so far this has been the greatest walkthrough for demonstrating the fuzzy system
Comment from : Neux

What a great explanation, once again!
Comment from : Antonio Arbues

Hey Brian, it is so wonderful to see that how you make literally anything look way simpler than they actually are! Looking forward to see the following sections!
Comment from : Levent Bilal