MATLAB House :: Channel

❇️Revolutionizing Multi-Robot Path Planning: Adaptive Differential Sine-Cosine Algorithm

In this video, we explore the innovative Multi-Strategy and Self-Adaptive Differential Sine–Cosine Algorithm in MATLAB, enhancing multi-robot path planning. Surpassing the traditional SCA, this approach introduces diverse strategies for better adaptability and performance, achieving a 42% improvement in navigating complex environments. Discover its application, comparisons with leading algorithms, and its potential to transform robotics.
🔻YouTube: https://youtu.be/4ZSgFP-G-jY
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#Robotics #PathPlanning #MATLABSimulation #AlgorithmImprovement #MultiRobotSystems #AdaptiveAlgorithms #SineCosineAlgorithm #MetaheuristicAlgorithms #EngineeringInnovation #TechExploration

❇️پروژه درس شناسایی سیستم

—شناسایی سیستم به صورت فضای حالت
—شناسایی سیستم با مدل های ARX , OE , BJ
—شناسایی غیر خطی NLARX ,...
—شناسایی جعبه خاکستری
—بهبود مدل با استفاده از همرشناین وینر
—شناسایی حوزه زمان سیستم با تولباکس شناسایی سیسیتم
—طراحی کنترل کننده پیش بین برای مدل شناسایی شده
—مقایسه کنترل کننده پیش بین با کنترل کننده PID همراه با نویز در خروجی و ورودی سیستم و ردیابی ورودی مرجع
—راهکاری ساده برای فیلتر نویز و...
دانلود:
https://npd.servr.ir/ident/pro/


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#MATLABCoding #ControlSystems #SystemIdentification #Modeling #NonlinearIdentification #PredictiveControl #PIDController #NoiseFiltering #Simulation #MATLABTutorial

تحلیل و طراحی کنترل موقعیت ربات یک درجه آزادی با استفاده از منطق فازی (تولباکس) در متلب

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✅Guidance and Navigation of Aircraft using Model Predictive Control

❇️FlightGear V3
❇️Simulink
❇️just run

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#AircraftNavigation #FlightControl #ModelPredictiveControl #FlightGearV3 #Simulink #AviationTechnology #AircraftGuidance #FlightSimulation #AerospaceEngineering #AircraftAutomation #AviationSimulation #FlightTraining #AutonomousFlight #FlightSimulationSoftware #AircraftSystems #AircraftDevelopment #AircraftTechnology #FlightGearCommunity #SimulinkModeling #AerospaceInnovation

Automated Driving Toolbox™
User’s Guide 2023a

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#Sensor #Vehicle #Pattern #Camera #Estimation #Labelling #App #Moving #Drawing #Automated #Simulation #Kalman_Filters #Map #Control #Velocity #NCAP #3D

❇️Track Multiple Vehicles Using a Camera❇️
This example shows how to detect and track multiple vehicles with a monocular camera mounted in a vehicle.

✅Overview
Automated Driving Toolbox™ provides pretrained vehicle detectors and a multi-object tracker to facilitate tracking vehicles around the ego vehicle. The vehicle detectors are based on ACF features and Faster R-CNN, a deep-learning-based object detection technique. The detectors can be easily interchanged to see their effect on vehicle tracking.

The tracking workflow consists of the following steps:

Define camera intrinsics and camera mounting position.

Load and configure a pretrained vehicle detector.

Set up a multi-object tracker.

Run the detector for each video frame.

Update the tracker with detection results.

Display the tracking results in a video.
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#Track #Vehicles #Camera #detector #intrinsics #Driving_Toolbox #R_CNN #deep_learning #ACF

نکاتی در مورد متلب 2023a
دارک مود ، نحوه دانلود و نصب ، برخی مزایا و معایب
نحوه دانلود و نصب کتابخانه و مثال های اماده از داخل متلب
نحوه استفاده از راهنما و مثال های آماده
اجرای مثال اماده یادگیری تقویتی پارک اتوماتیک
اجرای مثالی از طراحی اپ
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Reinforcement Learning Toolbox™
User's Guide 2023a

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#Reinforcement_Learning #Toolbox #RL #2023a #Environments #Designer #Simulink #Agents #Q_Learning #SARSA #Deep_Q_Network #Policy #Actor #Deep_Deterministic_Policy_Gradient #TD3 #PPO #TRPO #MBPO #NN

❇️General Fuzzy C-Means Clustering with Objective Function Control
In this MATLAB tutorial, we explore the General Fuzzy C-Means (GFCM) clustering strategy, a novel approach from the IEEE Transactions on Fuzzy Systems that enhances the traditional fuzzy C-means clustering by using an objective function to control fuzziness. This method improves clustering precision by providing a clear definition of fuzzy degree, enabling exact control over results. We demonstrate the GFCM algorithm's adaptability across various distance metrics and fuzzy degrees, emphasizing the importance of choosing the right fuzzy degree. The tutorial covers theoretical basics, practical applications, and the algorithm’s convergence and stability, offering valuable insights for students, researchers, and professionals in data science and machine learning.
🔻YouTube: https://youtu.be/o9DxlIYMNM0
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#FuzzyClustering #DataScience #MachineLearning #IEEE #FuzzySystems #Clustering #ObjectiveFunction #GFCM

❇️Fast Self-Supervised Clustering With Anchor Graph
This tutorial showcases the Fast Self-Supervised Clustering method for large-scale, high-dimensional data analysis without labeled samples, using MATLAB. It introduces the Fast Self-Supervised Framework (FSSF) and Balanced K-Means-based Hierarchical K-Means (BKHK) with bipartite graph theory. The method involves four key steps: acquiring an anchor set with BKHK, constructing a bipartite graph, solving the problem using FSSF, and selecting representative points for label propagation. Demonstrated to surpass other methods in performance and efficiency, it offers key insights for those in machine learning and data science.
🔻YouTube: https://youtu.be/_HgnVNGY5gQ
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#MachineLearning #MATLABSimulation #SelfSupervisedClustering #AnchorGraph #IEEE #DataScience #ClusteringAlgorithms #UnsupervisedLearning #BigData #AIResearch

نکاتی در مورد تحلیل آماری و بهینه سازی کد

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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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#Code #MIMO

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#clustering
#Ieee_Transactions
#Fuzzy
#Neural_Networks
#GA
#Coming_Soon

❇️Comprehensive Guide to Multivariable Control: From Differential Equations to QFT Controllers
This tutorial offers an in-depth look at multivariable control systems, particularly within electric arc welding, covering from basic principles like differential equations and block diagrams to advanced topics such as system dynamics, controllability, and advanced control strategies like H-infinity and LQR controllers. It emphasizes the Quantum Field Theory (QFT) controller's role in effectively managing complex control challenges. Designed for students, educators, and engineers, the video bridges theoretical concepts with practical applications, making it a key educational tool in control engineering.
🔻YouTube: https://youtu.be/uB9cJTalCuA
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#MultivariableControlSystems #ElectricArcWeldingControl #DifferentialEquations #StateSpaceModeling #HinfinityController #PIDTuning #LQRController #QFTController

Reinforcement Learning in Gridworld: Solving the Windy Grid Problem

Watch this video showcasing the implementation of a reinforcement learning algorithm in solving the Windy Grid Problem. The algorithm uses Q-learning with epsilon-greedy exploration to navigate a gridworld with varying wind powers. Learn how the agent learns to reach the goal by optimizing its actions based on rewards and Q-values. The video includes visualizations of the grid, wind powers, and the agent's path.

YouTube: https://youtu.be/AiI_4flFmYc

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#ReinforcementLearning #Qlearning #Gridworld #WindyGridProblem #ArtificialIntelligence #MachineLearning #CodingTutorial #Python #Algorithm #AI

MATLAB online

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#MATLAB #online #MATLAB_online

Genetic Algorithm Optimization in MATLAB: Visualizing Fitness Progression

In this video, we showcase the implementation of a Genetic Algorithm (GA) optimization technique using MATLAB. The GA is applied to optimize a 2-variable function by iteratively evolving a population of candidate solutions. The video demonstrates the fitness progression over generations, with the best, worst, and average fitness values plotted. The algorithm incorporates selection, crossover, and mutation operations to drive the evolution of the population. The function landscape, population, and elite individuals are visualized using contour plots and scatter points. Watch this video to gain insights into how a GA can be utilized for optimization tasks and witness the evolution of the population towards finding optimal solutions.

YouTube: https://youtu.be/SJ1zXyEbl0M

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#GeneticAlgorithm #Optimization #MATLAB #FitnessProgression #EvolutionaryAlgorithms #AlgorithmVisualization