ANFIS: Neuro-Fuzzy Inference System (Theory and MATLAB Implementation)

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Hello viewers, in this video, The Neuro-Fuzzy modelling highlighting ANFIS is explained. The basic theory of ANFIS is presented. Also the complete process of MATLAB implementation is given. In MATLAB implementation, the ANFIS is used as universal approximator. The two functions 1D sin(t) and 2D sin(r)/r are realized using ANFIS.
This video includes following contents,
* Neuro - Fuzzy Modelling.
* Adaptive Neuro-Fuzzy Inference System (ANFIS).
* ANFIS Architecture.
* ANFIS Hybrid learning algorithm.
* ANFIS Applications.
* ANFIS as Universal Approximator (UA).
* MATLAB Implementation of ANFIS as UA (with MATLAB code).
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1. Previous video:
"Fuzzy Logic Controller (FLC)": • Understanding Fuzzy Lo...
2. Link for research paper of Jang: www.dca.ufrn.b...

[@jmturupe]

nowadays, when you see a tutorial with Indian accent, you know it has better chances to be a good one. This time it didn't disappoint. Kudos to Indian engineering, Kudos to Dr. Verma

[@듀듑-o6l]

I have a question. Why does the form of fi in layer 4 give the formula of px+qy+r? If the input value is added (e.g. z) Does the formula of px+qy+kz+r? (k is parameter)

[@hassankianian2727]

Found your video at the perfect time when I needed the most. Thank you

[@sumbalsharfmemon1454]

Sir kindly make a video on 'Implemetation of ANFIS For fault detection in power system.

[@shahedthabitmalkawi8690]

والله كلامك جميل جدا وسهل لقد فهمت كل شي عن ANFIS MODEL. I'm Shahed, a master's student in Jordan; thank u so much. im speak Arabic but i wrote in English to express to u

[@prempyara3412]

Surely a useful presentation.

[@HARSHIT1506]

Sir is there any advance version of ANFIS which have less computational complexity and we can apply on sensor data to get predictions

[@abhinayhindu1827]

Sir matlab ki full video upload kigiye

[@atsu8057]

Well explained...

[@drs.g.k.gandhiadimt1056]

Useful content.

[@muhammadsufian7212]

Can u share how design Reactive current PI-fuzzy for a three-phase grid-connected PV

[@kdas4029]

Really valuable 👍

[@jeremyjaymagdaong6401]

Can hou clarify why we only need 2 nodes in layer 2? Shouldn't it be 4 since its 2^2? Thanks!

[@onkarchaudhary1233]

did you get why it is like that? i have the same doubt

@@onkarchaudhary1233 me too bro. I think the equation RF = (mf)^n is not corrected. It should be RF = mf * n

[@talkingbirb2808]

yes, there should have been 4 nodes. We should have one rule for every combination of formulas, as we can see on the picture at 17:45. So, the missed nodes would contain A1 X B2 and A2 X B1

[@patiphanjuijerm2052]

Nice explanation, thank you very much How do we know whether the model was overfitting or not?

[@exploringtechnologies9]

We can also check for overfitting. But that, I have not explained.

[@surjadeka31]

Nicely Done!.... Clear and detailed. Please keep making this type of video.

[@barisukauebari8563]

Thank you for the great lecture. Please I need detail training. And application to control system.

[@saurabhgupta9232]

Useful content.

[@sunitaverma5352]

Super presentation

[@tannguyentrong1142]

thanks u, very easy to understand, althought my eng is not good

[@syeda8343]

Brilliant🎉

[@연뚜리-r9w]

I'm curious about the reason why we don't have to write rules for fuzzy inference. Does ANFIS work with no rules? Or, does it create rules automatically? (If ANFIS work with rules, does 'fuzzy logic' have its own significance? )

[@squarehead6c1]

Athough I enjoyed this presentation as I got to know details about the learning process that I didn't know before, my reaction was similar to yours. The primary purpose of using the ANFIS is probably not to use it as a universal function approximator (standard ANNs are probably a better choice for that, both in terms of flexibility and efficiency), but to explicitly exploit domain knowledge through a fuzzy rule set. What we should be able to hope for is 1) to control the learning to conform to domain knowledge and 2) to exploit training data to fine tune the domain knowledge. And I still believe (without having tried it out, admittedly) that can be achieved. I searched the Internet and it seems that there is an addRule function that can be used for this purpose. From this presentation, interestingly, it seems that one does not have to specify rules, in which case you just get some free parameters (embedded in fuzzy rule structures) which can be learned during the training (in a very specific ANN structure). But if you extract the rules in the end (after training), it seems unlikely that they will contain much semantic information.

[@megherbich5746]

Thanks a lot. well explained very useful

[@MuhammadAdriBayuNurseto]

Can u make some data prediction ANFIS? With matlab?

[@exploringtechnologies9]

Times series prediction video is already on my channel.

[@deepakbhardwaj4018]

THANKYOU SIR

[@matinislami7893]

thank for your video I have a question, from which paper you said that FIS are not able to learn? I really need the refrence

[@exploringtechnologies9]

Fuzzy system works on pre defined IF-THEN rules. Once they are defined, they are not changed. While Neural network has weights which are updated with training.

[@matinislami7893]

@@exploringtechnologies9 thank for your response, I understand it.

[@prabhakarasharma7662]

Thank your sir

[@nadjemtech4963]

thank you ❤ Dr

[@doctorcollector8676]

Thank you so much Dr. This 30++ minutes video has given me 50% understanding on ANFIS theory & implementation.

[@evgenymalinovsky]

Thank you so much, sir. I've discovered your videos right when I needed to build fuzzy logic applications in Matlab.

[@syeda8343]

very good