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⚜️Neural network course session three::
3️⃣An Illustrative Example

🔵In this MATLAB tutorial, learn how to implement Principal Component Analysis (PCA) and Anchor Graphs for dimensionality reduction. The video covers the core concepts, provides step-by-step code explanations, and demonstrates how to visualize and compare results. By the end of this tutorial, you'll be able to apply PCA and Anchor Graphs to your own datasets in MATLAB. Suitable for both beginners and experienced users.

✅Visualizing PCA results in MATLAB
✅Introduction to Anchor Graphs and their advantages
✅Constructing Anchor Graphs in MATLAB
✅Using Anchor Graphs for efficient dimensionality reduction
✅Comparing PCA and Anchor Graph results
🔻YouTube: third session

Download file and codes (in comment)::
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% Doc This Code
%% Part 1
% Differential Equations Line 46
% Block diagram Line 76
% State space equations Line 110
% transformation function matrix Line 125
% Description of matrix fraction Line 130

%% Part 2
% System_Pole Line 156
% SmithForm_G Line 162
% MacMilan_pole Line 168
% Zero_Element Line 172
% Zero_transfer Line 181
% Zero_decoupling Line 185
% Controllability and Observability Line 199
% Norm_2 , Norm_infinitely , Norm_Henkel Line 210
% Realization of system balance Line 231
% Reduction of Order Line 238
% Igenvalues of Frobenius Line 262
% Grishorian bands Line 288
% Nyquist Plot Line 303
% Gain-Space Diagram Line 321

%% Part 3
% H-infinity controller Line 357
% PI with pidtune Line 425
% PI 2 (sigma) Line 477
% LQR Controler Line 553
% Optimal LQR with H inf Line 620
% QFT Controler Line 684
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