Fuzzy Logic Controller Tuning | Fuzzy Logic, Part 4

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  • Опубликовано: 8 сен 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...
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Комментарии • 24

  • @nazstreamsini4870
    @nazstreamsini4870 4 месяца назад +5

    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

  • @idontwantoregister
    @idontwantoregister Год назад +6

    amazing series - never been more excited to watch a whole series

  • @IbroAlis
    @IbroAlis 2 года назад +12

    Amazing lecture series from the best lecturer! Hope there will be more videos on the topic

  • @artemis818_
    @artemis818_ 9 месяцев назад

    Thank you so much for your fuzzy logic series, I learned a lot.

  • @yeshiwangdi4775
    @yeshiwangdi4775 2 года назад +3

    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.

  • @Pedritox0953
    @Pedritox0953 2 года назад +3

    Very interesting

  • @bbbbr5304
    @bbbbr5304 2 года назад +5

    Cool, when's the next lesson?

  • @conorkeenan7278
    @conorkeenan7278 Год назад

    Thanks Brian!

  • @kanhubhayya8414
    @kanhubhayya8414 Год назад

    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 .

  • @knnurl
    @knnurl Год назад

    amazing as always!

  • @alexanderskusnov5119
    @alexanderskusnov5119 2 года назад +2

    Could you explain the difference between genetic and surrogate optimization? In my case the surrogate method calculates longer but gets better results.

  • @sodiko100
    @sodiko100 2 года назад +2

    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

    • @9okku
      @9okku 2 года назад

      neuro-fuzzy systems use neural networks which is offline data training so unfortunately, it is not real-time learning

  • @xwyluislara2970
    @xwyluislara2970 2 года назад +1

    I have a question, is it possible to include a genetic algorithm to optimize the membership functions of a neuro fuzzy network?

  • @hakemass
    @hakemass Год назад

    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 ?

  • @hasinabrar3263
    @hasinabrar3263 2 года назад

    Please suggest a book on fuzzy intelligence system.

  • @ghadarhuma3322
    @ghadarhuma3322 2 года назад

    Hi thank you for this, where can I find this example on MATLAB, i want to try it but I couldn't find it

  • @surflaweb
    @surflaweb 2 года назад +1

    I'll it use to temperature controll.

    • @spaceflight1019
      @spaceflight1019 Год назад

      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.

  • @minhajsixbyte
    @minhajsixbyte Год назад

    Can't find any link to the writeup

  • @santiagomeza25
    @santiagomeza25 Год назад

    Somebody give class about this?

  • @jpenneymrcoin6851
    @jpenneymrcoin6851 Год назад +1

    "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.

  • @pnachtwey
    @pnachtwey 4 месяца назад

    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.