What Is Fuzzy Logic? | Fuzzy Logic, Part 1 

every time i strumble upon a new video and see master douglas talking I know beforehand the video will be awesome!

Wow, just wow! Outstanding explanation! I have just started watching your videos and they are both detailed and understandable. Thank you for all 🙏

Sir, everytime you upload matlab tech talk video, it always relevant to my current studies. It's like you always hear my inner voice: "can someone explain about this and that to me, clearly and catchy?". Then, i became more confidence that I am in the rght learning path. Very big thanks, sir. 💙

Very interesting talk, and easy to understand! Just to say the fuzzification process explained here is a little bit different from my understanding. The fuzzificaion is to convert a crisp number into a fuzzy set. It can be singleton or non-singleton. Your explanation is based on singleton, and taking one step further to get the compatibility (which is then used as rule firing strength) to rule antecedents. But, anyway, I would say your explanation is good enough as an illustration for beginners to get some ideas about how the whole fuzzy system works. Thanks for sharing these. It’s really useful!

Hi sir, big fan of your teaching. Fell in love with control systems after watching your videos. Thank you so much sir❤

Over the decades, I dabbled with fuzzy logic a couple of times. The base modus operandi of fuzzy logic aside, it is always interesting to think about the way, fuzzy logic is presented to a user within a programming language. Being currently in one of my iterations, thinking about fuzzy logic implementations, I came to the conclusion, that the "IF ... THEN ... " syntax is not really intuitive at all. The video uses the mandami approach, as opposed to the TKS approach. Both of which differ in the THEN term. One of the benefits of Common Lisp is its macro system. And while I started out with the "IF THEN" syntax, I finally converged on something I find more... true to what is actually happening: As an example (for your amusement and me getting some productive feedback, maybe), here the "bot" implementation for a silly and simple dice game, where the bot needs to choose between 2 actions, depending on the game state, which is a 4 tuple of values. The function uses my nifty "fuzzy" macro... lets see if people can intuit, how the function works... (defun ddg-fuzzy-bot-choose-action (opponent-score own-score turn-score last-roll) (let ((low-roll (define-f-var '(1 1.0) '(4 0.0))) (high-roll (define-f-var '(2 0.0) '(5 1.0))) (behind (define-f-var '(-20 1.0) '(0 0.0))) (ahead (define-f-var '(0 0.0) '(20 1.0))) (even (define-f-var '(-5 0.0) '(0 1.0) '(5 0.0))) (high-stake (define-f-var '(2 0.0) '(10 1.0))) (low-stake (define-f-var '(0 1.0) '(4 0.0)))) (let ((score-diff (- (+ own-score turn-score) opponent-score))) (fuzzy ;; associate input-variables with membership functions ((turn-score (low-stake high-stake)) (last-roll (low-roll high-roll)) (score-diff (behind even ahead))) ;; define he fuzzy rules ((should-roll (or (and ahead low-stake low-roll) behind (and even low-roll))) (should-stop (and ahead (or high-stake high-roll)))) ;; use the fuzzy output variables. (values (if (>= should-roll should-stop) (then :roll) (else :stop)) (list :should-roll should-roll :should-stop should-stop)))))) The FUZZY macro helps with associating membership-functions to input variables and to define the rules for its output variables "should-roll" "should-stop" in a lispy way. The third part in that macro is just the code, which uses the computed output variables for its decision making. Behind the scenes, the OR and AND operations are simply replaced with MAX and MIN, respectively. (should-stop (and ahead (or high-stake high-roll))) would be written traditionally with something like this: IF ahead AND (high-stake OR high-roll) THEN rather should-stop And I would also have to define a set of membership functions for the "should-stop" fuzzy variable. It adds a lot of complexity, which might not often be required. Also I find it difficult to come up with those membership functions for the outputs.

really clear and easy to follow explanation, thank you so much!

explaining the steps with the example cleared most of my doubts. Thank you!

Awesome video. Very effective teaching of the subject. Thank you!

Simply Marvelous job Mr. Brian. Thank you very much

Easy to understand. Great job, mate!

Was eagerly waiting for this! Great video

Always deeply appreciate ur series. Thank u so much

Thanks for this video. It has given me a better understanding of fuzzy logic.

I truly love this clip. Inspire me a lot for work. - from Thailand

Great Explanation ! One doubt is that when you you do defuzzification of fuzzy variables= [0 0.9 0.1] Don't we chop first graph at 0% , second graph ( Medium) at 90% and last one at 10 % . Why you have chopped the first graph( low membership) at 10 % it should be 0%. Require your expert comments on this .

The explanations are so clear!

One of my favorite subject explained by one of my favorite Controls teacher. Amazing! Wonder if you're going to introduce more advanced topics on the series, especially type-2 fuzzy logic

It's extremely easy to follow you. Please keep it that way. 🙏

Hello Mr.Douglas 😊

I just started studying on this topic and saw your video. A great video to understand the logic. Thank you !

A talent of control explanation!

Man, You really ROCK!!! It is very easy to understand, despite reading books😊

Great video!

Comprehensive and simple.

Great explanation.

good job Brian

Thanks a lot! Quite sad that fuzzy logic application do not get much credits industry-wise nowadays.

take all of my data and tell me the theory of everything, thank you

great explanation

Great work

Amazing video

Fuzzy logic = Daily way of thinking! ♡♧◇☆♡

Hey, i'm a computer engineering student at UTFPR Curitiba and i've participated in extension courses in the past that were basically translating videos like these to portuguese, submitting the subtitles to the platform and then having it available in multiple languages. We'd then receive a certain amount of hours in complementary activities that we require to graduate. Is there an e-mail or form that one could use to get in contact with matlab or the teacher responsible for these videos to formalize an extension course inside UTFPR to subtitle these four videos to portuguese? I'm sure this material would help a ton of students all across Brazil and many more countries!

Amazing tutorial

Well done Mr. Douglas. Please, I require serious help with my project. I am working on intelligent traffic light system using a fuzzy inference engine. Please, how will a fuzzy logic program be written and connected to a traffic light system?

If you ever make a full video course on this for control engineering people, please take my money.

Tanks a lot!

Thanks for the explanation But how to know where the centroid of the shape should be ?

Thank you

Fantastico!

Waiting for Part 2

fuzzy here, fuzzy there, my brain goes fuzzy fuzzy. hahaha

gracias!

Thanks For the video. Can I apply it on online gambling games😅😅?

Nice!

A good video ❤

Thanks

Thank you And what about Neuro fuzzy

here! take my like!

13:40 Interesting that there is never 0 risk...

How is this different from point systems (like XP, health, etc) in videogames or analog control systems? Are those both examples of 'fuzzy logic'?

pls how can i resolve this fuzzy Error using FuzzyInferenceSystem (line 288) min is a script. Error in mamfis (line 165) fis = fis@FuzzyInferenceSystem(varargin{:}); Error in fuzzy (line 30) action = mamfis('Name','Untitled');

Really great explanations of a very fuzzy topic!!! Is there a link to the matlab banking example in the video and to the second part of this series?

I failed fuzzy logic in Uni :) Still best course i took ;)

Is this like PID: proportional, integral, differential?

Brian Douglas is a boon for people who want to learn Control Systems

Can anyone tell me what Abbreviation NDS stands for ?

Brain is the OG

THE BATLOGIC CONTROLLER

Fuzzy Logic is multi valued language with membership functions. Red Pill. Matrix Crew. Topology.

Sow how convert the 46% to binary again

When fuzzy becomes less fuzzy: Consider the Twilight Zone episode where the banker is ready to grant a loan, but doesn't know that the borrower wants to bet the loan on several horse races. That is, until Hector B. Poole who can read minds overhears the borrower's thoughts in A Penny For Your Thoughts.

Awsome , thank u , could u plz give us these presentation pdfs or powerpoints ?

The world is full of fuzzy logic people.

can we say fuzzy logic is a kind of machine learning?

Just give good tips men..

Does fuzzy logic deny the law of non excluded middle?

Fuzzy logic was a bear. Fuzzy logic had 0.2 hairiness.

This was still too fuzzy for us!

How are you caculated 46 percent% ?

Ok

You are Brian? Or are your really Brian?

Lütfü Aliasker Zade

Nice introduction without mathematics :-)

i do not understand why %46 for this example: 12:01

Thanks