Language
English
عربي
Tiếng Việt
русский
français
español
日本語
한글
Deutsch
हिन्दी
简体中文
繁體中文
API
Home
How To Use
Language
English
عربي
Tiếng Việt
русский
français
español
日本語
한글
Deutsch
हिन्दी
简体中文
繁體中文
Home
Detail
@eyeplusnews: Lương mà 50 triệu thì còn cỡ nào nữa #eyeplusnews #eyeplusmedia #tiktoknews #tinnong #tinmoi #news #tinmoi24h #tinnhanh #tintuc #haihuoc #vuinhon
Eyeplus News
Open In TikTok:
Region: VN
Sunday 27 September 2026 07:30:00 GMT
51045
544
10
37
Music
Download
No Watermark .mp4 (
3.45MB
)
No Watermark(HD) .mp4 (
2.54MB
)
Watermark .mp4 (
4MB
)
Music .mp3
Comments
Ranjan mk :
Món ăn ngon nhất ở Việt Nam là gì?
2026-09-27 08:30:25
1
Ⓣⓞⓐⓢⓣ❄️ :
đầu
2026-09-27 07:34:44
0
con ni nqu💗 :
sớm..
2026-09-27 07:33:48
0
68gb săn lộc mỗi ngày :
Vậy ai trả lương cao cho đó thôi
2026-09-27 22:20:22
0
văn :
2026-09-27 09:55:12
0
ZO88 - UY TÍN TẠO NIỀM TIN :
Món ăn ngon nhất ở Việt Nam là gì?
2026-09-27 13:23:27
0
nguyen Mitt :
🥰🥰🥰
2026-09-27 09:49:23
0
To see more videos from user @eyeplusnews, please go to the Tikwm homepage.
Other Videos
USE HEADPHONES 🎧 FOR BEST EXPERIENCE 🎶🎵🎧 IM MUSIC 🎶🖤🎧 #bass #bassbosted #aveeplayer #teranding #music
dùng rồi mới hối hận vì ko mua sớm hơn..siêu dai dùng trộn gỏi, làm đồ ăn, dọn rửa tiện gì đâu...#baotay #baotaynilong #baotaytopgia #gangtaynhabep
I always come back🔪 TGK | ENDLESS_STORY56💕 ⚠️FAKE BLOOD⚠️ #screammovie #вреки #scream #ghostface #ghostfacecosplay
#لايك_متابعه_اكسبلور #هشتاقات_تيك_توك
Start with the simplest possible model: $$ y(t)=\theta\,u(t) $$ At first, estimating \(\theta\) looks trivial: $$ \hat{\theta}=\frac{y}{u} $$ But in practice, this becomes dangerous near zero crossings and in the presence of measurement noise. Least Squares solves this by accumulating information over time. Recursive Least Squares turns that idea into a real-time estimator. And then comes the interesting part: Instead of using the full time-varying inverse covariance \(P(t)\), many adaptive control laws replace it with a constant gain \(\gamma\). You give up part of the estimator’s statistical optimality — but gain much simpler error dynamics: $$ \dot{\phi}=-\gamma u^2(t)\phi $$ That makes the closed-loop stability analysis far more transparent. But even a perfectly stable estimator cannot learn if the system provides no information. That is where **Persistent Excitation** becomes essential: No sufficiently rich input → no new information → no guaranteed parameter convergence. In short: **Stability does not guarantee learning. Learning requires excitation.** Swipe through the carousel for the full path from direct division → Least Squares → continuous-time RLS → gradient estimation → Persistent Excitation. #ControlTheory #AdaptiveControl #SystemIdentification #RLS #LeastSquares
omg 👇
About
Robot
API
Legal
Privacy Policy