Fuzzy Logic Controller Tuning | Fuzzy Logic, Part 4
Вставка
- Опубліковано 19 вер 2024
- Cover the basics of data-driven approaches to fuzzy logic controller tuning and fuzzy inference systems.
See how to tune fuzzy inference parameters to find optimal solutions. Learn how optimization algorithms, like genetic algorithms and pattern search, can efficiently tune the parameters.
Follow along with an example about tuning a fuzzy inference system using data that controls an artificial pancreas.
Fuzzy Logic Toolbox: bit.ly/3P7v1jw...
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2022 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See www.mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
this series has been crazy good! I almost gave up after my first video of fuzzy logic that i watched was a video of a speaker just reading the article titled "what is fuzzy logic". I am glad i did not give up to find a better video
immensely relatable
amazing series - never been more excited to watch a whole series
Amazing lecture series from the best lecturer! Hope there will be more videos on the topic
Thank you so much for your fuzzy logic series, I learned a lot.
Great tutorial Sir😁
Could you make a vedio on electricity load forecasting using the fuzzy logic data driven and finding the optimal rule base and MF using genetic algorithim please..😁
It would be very help full.
Cool, when's the next lesson?
superb video sir, Come to know much more things in just a single video.
Can you please share the link of your code , so that we can implement those things .
amazing as always!
Could you explain the difference between genetic and surrogate optimization? In my case the surrogate method calculates longer but gets better results.
Very interesting
Thanks Brian!
Superb video. I am curious on the difference between genetic fuzzy and neuro fuzzy. Are genetic algorithms used for tuning while neuro fuzzy is for real time learning ? They feel similar to me and I can't find a source describing both if then clearly
neuro-fuzzy systems use neural networks which is offline data training so unfortunately, it is not real-time learning
I have a question, is it possible to include a genetic algorithm to optimize the membership functions of a neuro fuzzy network?
Please suggest a book on fuzzy intelligence system.
at 16:00 is VH and H for pre calculated dose are the same ? why we can't find VH in the possible cases of the pre calculated dose ?
Can't find any link to the writeup
Hi thank you for this, where can I find this example on MATLAB, i want to try it but I couldn't find it
I'll it use to temperature controll.
Fuzzy logic, like today's AI, was THE buzzword in digital industrial controllers about 35 years ago. During the initial tuning process, they had a sometimes dangerous propensity to run a process's final control device to the limits in order to learn the system's response times. Most processes couldn't stand such upset and you wound up adjusting the tuning constants manually.
Somebody give class about this?
"what is optimal" - oh, well, you have a cost function and whatever minimizes that cost is optimal...
uh, so whatever you decide is optimal, is optimal?
oh. well. yeah.
So just say that then.
The first question should be, "Why bother?" LQC, is much better than Fuzzy Logic. Fuzzy Logic is or was a fad. That I like doing is debunking Fuzzy Logic advocates that write papers showing how Fuzzy Logic is better than traditional PID control. The instructors are wasting the students time and MONEY.