🔹مجموعه تولباکس های متلب شامل:
🔵5G Toolbox™ User's Guide 2021b

🟢Aerospace Toolbox User's Guide 2021b

🟡Bioinformatics Toolbox™ User's Guide 2020a

🔴Communications Toolbox™ Getting Started Guide 2022b

🟠MATLAB® Compiler™ Excel® Add-In User's Guide 2021b

🟤Data Acquisition Toolbox™ User's Guide 2017a

⚪️Database Toolbox™ User's Guide 2022a

⚫️Deep Learning HDL Toolbox™ User's Guide 2021a
⚫️Deep Learning Toolbox™ Reference 2022a
⚫️Deep Learning Toolbox™ User's Guide 2020a

🟣MATLAB® Distributed Computing Server™ System Administrator's Guide 2016a

🔵Fuzzy Logic Toolbox™ User's Guide 2016a
🔵Fuzzy Logic Toolbox™ User's Guide 2015a
🔵Fuzzy Logic Toolbox™ User's Guide 2020a
🔵Fuzzy Logic Toolbox™ User's Guide 2018a

🟢Global Optimization Toolbox User's Guide 2015b

🟡Image Processing Toolbox™ User's Guide 2021a
🟡Image Processing Toolbox™ User's Guide 2020a

🔴Neural Network Toolbox™ User's Guide 2015b

🟠MATLAB® Object-Oriented Programming 2017a

⚪️Optimization Toolbox™ User's Guide 2015b
⚪️Optimization Toolbox™ User's Guide 2020a

⚫️Parallel Computing Toolbox™ User's Guide 2016a

🟣MATLAB® Primer 2015b
🟣MATLAB® Primer 2018a

🔵Reinforcement Learning Toolbox™ User's Guide 2020a

🟢Robust Control Toolbox™ User's Guide 2017a
🟢Robust Control Toolbox™ User's Guide 2015b

🟡Sensor Fusion and Tracking Toolbox™ Reference 2021b

🔴Signal Processing Toolbox™ User's Guide 2015b

🟠SimEvents® Getting Started Guide 2015b

🟤SimMechanics™ Getting Started Guide 2015b

⚪️Simulink® Graphical User Interface 2022b
⚪️Simulink® Developing S-Functions 2017a
⚪️Simulink® User's Guide 2015b

⚫️Statistics and Machine Learning Toolbox™ User's Guide 2021a
⚫️Statistics and Machine Learning Toolbox™ User's Guide 2020a
⚫️Statistics and Machine Learning Toolbox™ User's Guide 2017a

🟣System Identification Toolbox™ Getting Started Guide 2016a

🔵Text Analytics Toolbox™ User's Guide 2020a

🟢Wavelet Toolbox™ User's Guide 2022b
🟢Wavelet Toolbox™ User's Guide 2015b
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#دوره_آموزشي مجازی نرم افزار متلب




⭕️با اعطای فیلم های آموزشی ریکورد شده

🔖صدور گواهي حضور
✅اعطای مدرک معتبر از سوی نماینده بنیاد ملی نخبگان

مدرس دوره:
👤مهندس ناصر پاکار
✅ دانشجوی ارشد برق گرایش کنترل در دانشگاه فردوسی مشهد


🔵مخاطبین دوره:
تمامی رشته های مهندسی علی الخصوص مهندسی برق و پزشکی.

⏱كارگاه 12 جلسه
24 ساعت تدریس

📆تاريخ شروع دوره شنبه 13 ام اسفند ماه

⏰زمان: 10 الي 12 روزهای جمعه

📀 لینک ویدیو ضبط شده جلسات در اختیار شما قرار خواهد گرفت.

تاریخ ثبت نام از ۳ لغایت ۱۲ اسفند ماه

ثبت نام از طریق سایت رویدادستان

https://rooydadestan.ir/?p=82745

🔵اطلاعات بیشتر 👇


🆔 @Rafieeofficial

@N_U_E_E_S

The video tutorial shows how to use Python and the OpenAI API to generate images from a chat. The steps include installing Python, choosing a coding environment, installing required libraries using pip, creating an API key by registering on the OpenAI website, and writing Python code in Visual Studio Code. The tutorial demonstrates generating different types of images using the API, specifying image types, and improving image quality. It is noted that the results may vary for the free version of the API.
Complete Version:
https://www.youtube.com/watch?v=jF5nEuePlqE

🆔 @MATLAB_House

@MATLABHOUSE

#Python #OpenAI #API #imagegeneration #visualstudiocode #Pillow #requests #chatbot #imageprocessing #computergraphics #artificialintelligence #machinelearning #tutorial #imagequality #imageoutput #programming #Python #API #OpenAI #image_generation #Visual_Studio_Code #Pillow #requests #programming #AI #machine_learning #computer_vision #deep_learning #natural_language_processing #chatbot #image_quality #tutorial

In this video, we demonstrate how to use MATLAB and MQL4 programming languages to forecast the price of gold in the forex market. We'll walk you through the process of time series analysis, which involves analyzing and modeling patterns in historical price data to make predictions about future trends.
https://www.youtube.com/watch?v=7zSKoqd1LXs

🆔 @MATLAB_House

@MATLABHOUSE

#GoldPriceForecasting #ForexMarket #TimeSeriesAnalysis #MATLAB #MQL4 #AlgorithmicTrading #Investment #Trading #ARIMA #GARCH #KalmanFilter #MATLAB_House #MATLABCommunity #MATLABLearning #MATLABCode #MATLABProjects #MATLABTips #MATLABTricks #MATLABHelp #Finance #Economics #DataScience #Programming #Coding #Technology #FinancialData #FinancialAnalysis #StockMarket #CommoditiesMarket #TradingStrategies #InvestmentStrategies #QuantitativeFinance #DataAnalytics #DataVisualization #MATLABAlgorithms #MATLABFunctions #MATLABCoding #MachineLearning #ArtificialIntelligence #DeepLearning #NeuralNetworks

✅"How Not To Destroy the World With AI"

Stuart Russell, Smith-Zadeh Chair in Engineering

Wednesday, April 5, 12:00 pm PDT

A Distinguished Lecture on the Status and Future of AI. Presented by CITRIS and the Banatao Institute and BAIR.
This event is open to the public, though in-person seating is currently sold out.

Please find the live-stream link here:

https://www.youtube.com/user/citrisuc/live

The CITRIS Research Exchange and Berkeley Artificial Intelligence Research Lab (BAIR) present a distinguished speaker series exploring the recent breakthroughs of AI, its broader societal implications and its future potential. All talks are free and open to the public.

https://www.berkeley.edu/ai/

❇️more in coment❇️

In this MATLAB programming example, we solve an optimal control problem using the Pontryagin's Maximum Principle. We use the state equations, cost function, Hamiltonian, and costate equations to obtain the optimal control. The solution is obtained using the "dsolve" function, and the results are visualized using MATLAB plots. This example is taken from the "Crack Optimal Control" book.

Watch the video on YouTube:
https://www.youtube.com/watch?v=eN9qZ-dOskM

telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#OptimalControl #MATLAB #PontryaginMaximumPrinciple #CrackOptimalControl #StateEquations #CostFunction #Hamiltonian #CostateEquations #dsolve #MATLABPlots #ControlTheory #Optimization #DynamicProgramming #NumericalMethods #Engineering #Mathematics #Coding #Programming #ArtificialIntelligence #MachineLearning #SymbolicComputation #Visualization #Simulation #MathModeling #Education #STEM

John Koza, a Stanford University researcher, developed genetic programming as a method to evolve computer programs by simulating the natural selection process. In this approach, a population of computer programs, composed of primitive functions and terminals, is evolved to solve a given problem. Each program's fitness is determined by its effectiveness in solving the problem. A few programs with high fitness are selected for reproduction, while many participate in a recombination operation called crossover. By iterating this process over multiple generations, the structure of a computer program that effectively solves the problem can emerge.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

@MATLABHOUSE

#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1

genetic programming, a method for computers to solve problems without explicit programming. Breeding randomly generated programs of different sizes and shapes, the fittest ones are selected for further breeding, creating better solutions over many generations. Stanford professor John Koza's research focuses on exploiting regularities and symmetries of complex environments for hierarchical organization and reuse. The ultimate goal is to enable computers to learn to solve non-trivial problems.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

@MATLABHOUSE

#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_2

John Koza from Stanford University discuss genetic programming, which automatically creates programs from problem statements. Results produced are competitive with human-produced ones and even infringe on previously patented inventions. Genetic programming is an extension of the genetic algorithm and starts with randomly generated programs that undergo fitness evaluation, selection, and genetic operations. The resulting programs solve a variety of problems, reuse steps, and produce non-trivial results.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

@MATLABHOUSE

#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_3

Discover the power of genetic programming in creating automated solutions for various problems such as controllers, antennas, genetic networks, and analog electrical circuits. The Genetic Programming IV book and video show how this approach can deliver high-return, human-competitive machine intelligence, and even create patentable inventions. With increasing computer time, results have progressively improved over 15 years. The video highlights the creation of a PID controller using genetic programming, emphasizing that results are human-competitive if they meet specific arm's length criteria.
Download from Iranian server (VPN and proxy must be disconnected)
This video has Persian subtitles and noise removal and quality enhancement.
🆔 @MATLAB_House

@MATLABHOUSE

#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_4

Comparative Analysis of Bandit Algorithms for Optimal Decision-Making

Explore a comprehensive comparative analysis of various bandit algorithms used in reinforcement learning for optimal decision-making. This video showcases the implementation and evaluation of different methods such as Greedy, Epsilon-Greedy, UCB, and more, highlighting their strengths and performance in selecting optimal actions. Gain insights into the trade-off between exploration and exploitation strategies and learn how these algorithms can enhance decision-making systems. Join us for a deep dive into the world of bandit algorithms and their applications.

YouTube: https://youtu.be/K2dPVza-pSQ


🆔 @MATLAB_House

@MATLABHOUSE

#ReinforcementLearning #BanditAlgorithms #DecisionMaking #ExplorationVsExploitation #OptimalActionSelection #MachineLearning #DataScience #AI #CodeImplementation #AlgorithmComparison #PerformanceAnalysis

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

🆔 @MATLAB_House

@MATLABHOUSE

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

MATLAB online

🆔 @MATLAB_House

@MATLABHOUSE

#MATLAB #online #MATLAB_online

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

🆔 @MATLAB_House

@MATLABHOUSE

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

❇️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
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#MultivariableControlSystems #ElectricArcWeldingControl #DifferentialEquations #StateSpaceModeling #HinfinityController #PIDTuning #LQRController #QFTController

🆔 @MATLAB_House

@MATLABHOUSE


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

% 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
🆔 @MATLAB_House

@MATLABHOUSE

#Code #MIMO

نکاتی در مورد تحلیل آماری و بهینه سازی کد

🆔 @MATLAB_House

@MATLABHOUSE

❇️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
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#MachineLearning #MATLABSimulation #SelfSupervisedClustering #AnchorGraph #IEEE #DataScience #ClusteringAlgorithms #UnsupervisedLearning #BigData #AIResearch