MATLAB House :: Channel

✳️Deep Belief Network Controller: A Modern Alternative to PID in Simulink

🔰Discover how to replace traditional PID controllers with advanced Deep Belief Network (DBN) controllers in Simulink. This tutorial demonstrates the step-by-step process of implementing a DBN controller, showcasing its advantages over PID in complex control systems. Learn how this cutting-edge AI technique can enhance system performance and adaptability across various engineering applications. Whether you're a control systems engineer, an AI enthusiast, or a student exploring advanced control methods, this video offers valuable insights into the future of intelligent control systems."

#DeepBeliefNetwork #ControlSystems #Simulink #MachineLearning #PIDController #AIControl #EngineeringTutorial #AdvancedControl #MATLAB #IntelligentSystems

⚜️Neural network course session

🔰Linear Control Training Workshop - Session 5

❇️ساختن ربات تلگرام مخصوص اطلاع اتمام شبیه سازی ها در متلب ❇️

با توجه به زمان‌بر و پیچیده بودن کدها، ایجاد سیستمی برای اعلام اتمام شبیه‌سازی‌ها اهمیت زیادی دارد. در این ویدیو، نحوه ساخت ربات تلگرامی ساده‌ای را آموزش می‌دهیم که از طریق API با متلب ارتباط برقرار می‌کند و پیغام پایان شبیه‌سازی را ارسال می‌کند. این روش به دلیل سادگی و حفظ حریم خصوصی بهتر از ارسال ایمیل است. هنگام اتمام شبیه‌سازی، تنها کافی است دستور sendTelegramMessage('Simulation completed successfully!'); را فراخوانی کنید. همچنین، کدی برای ایجاد هشدار صوتی در سیستم‌های شخصی پس از اتمام کد نیز قرار داده شده که در تابع soundtest قرار دارد و قابل فراخوانی است. تمامی کدها در کامنت‌ها شرح داده شده‌اند.

🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#MATLAB #TelegramBot #Simulation #Automation #TechTutorial #Engineering #Coding #SoftwareDevelopment #APIIntegration #TechTips

🔰Linear Control Training Workshop - Session 4

🔵In this MATLAB tutorial video, we dive into the powerful control systems analysis and design capabilities of MATLAB. Learn how to create and interpret root locus plots to analyze system stability and transient response characteristics. We then explore various control system compensation techniques, including lead, lag, and lag-lead compensation, and how to design compensators using the root locus approach.

🔸Generating root locus plots with MATLAB
🔹Effects of poles and zeros on root locus shape
🔸Finding gain values at points on the root locus
🔹Plotting root loci with damping ratio and natural frequency lines
🔸Lead compensator design
🔹Lag compensator design
🔸Lag-lead compensator design
🔹Analyzing compensated vs. uncompensated system responses
🔸Parallel compensation and velocity feedback
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE

#MATLAB #ControlSystems #RootLocus #SystemStability #LeadCompensation #LagCompensation #LagLeadCompensation #ControlSystemDesign

👫معرفی 7 دوره جدید رایگان از دانشگاه هاروارد برای کسانی که به دنبال یادگیری مهارت‌های جدید یا ارتقا خودشون هستند:
@bmniran

🔎1. Introduction to Computer Science
✔️یه دوره رایگان 12 هفته‌ای که به 6 تا 18 ساعت در هفته زمان برای یادگیری نیاز داره و مبانی برنامه نویسی را معرفی می‌کنه. تو این دوره در مورد الگوریتم‌ها، ساختارهای داده، مهندسی نرم افزار، توسعه وب و زبان‌های برنامه نویسی صحبت شده.
https://edx.org/learn/computer-science/harvard-university-cs50-s-introduction-to-computer-science?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8Xww1WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎2. Introduction to Artificial Intelligence with Python
✔️یک دوره مقدماتی در زمینه هوش مصنوعی با پایتون، که مدت زمانی تقریبا 7 هفته‌ای نیاز داشته و لازمه هر هفته 10 تا 30 ساعت وقت بگذارید براش و در زمینه گراف‌ها، یادگیری ماشینی و شبکه های عصبی صحبت شده و یاد میده که پروژه‌های عملی را با استفاده از پایتون انجام بدین.
https://edx.org/learn/artificial-intelligence/harvard-university-cs50-s-introduction-to-artificial-intelligence-with-python?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8X1g1WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎3. Data Science: Machine Learning
✔️این دوره هم همونطور که از اسمش مشخص هست در مورد مباحث ماشین لرنینگ هست و تمرین های خوبی رو هم داره که کامل بتونین مسلط بشید.
https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8X3w1WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎4. Data Science: Productivity Tools
✔️یک دوره 8 هفته‌ای است که برای یادگیری و سازماندهی پروژه‌ها کمک کننده هست و هفته ای 1 تا 2 ساعت نیاز داره برای یادگیری.
https://edx.org/learn/data-science/harvard-university-data-science-productivity-tools?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8Xy41WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎5. Web Programming with Python and JavaScript
✔️دوره‌ای که برنامه نویسی وب رو با پایتون آموزش میده و 12 هفته زمان دوره هست و لازمه 6 تا 9 ساعت در هفته رو بهش اختصاص بدین و در این دوره طراحی وب اپلیکیشن هم وجود داره.
https://edx.org/learn/web-development/harvard-university-cs50-s-web-programming-with-python-and-javascript?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8XU81WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎6. Introduction to Game Development
✔️این دوره 12 هفته‌ای هم برای کسانی که علاقه مند هستند تا برنامه نویسی و توسعه گیم و بازی رو شروع کنن خیلی جذاب میتونه باشه.
https://edx.org/learn/game-development/harvard-university-cs50-s-introduction-to-game-development?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8XW01WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1

🔎7. Introduction to Cybersecurity
✔️این دوره هم همونطور که از اسمش مشخصه در مورد امنیت سایبری هست و 5 هفته زمان میبره تا دوره رو به اتمام برسونین.

https://edx.org/learn/cybersecurity/harvard-university-cs50-s-introduction-to-cybersecurity?irclickid=WhA1hk2lDxyPT1IyXUS9p1tJUkHW8XQY1WyGXQ0&utm_source=affiliate&utm_medium=Guiding%20Tech%20Media&utm_campaign=Online%20Tracking%20Link_&utm_content=ONLINE_TRACKING_LINK&irgwc=1


#آموزش_علمی
#نخبگان_ایران

⚜️Neural network course session four::
4️⃣Perceptron Learning Rule

🔵In this MATLAB tutorial video, we dive into the fundamentals of the Perceptron Learning Rule, a powerful algorithm for training single-layer neural networks. Through practical examples and step-by-step explanations, you'll learn how to implement the Perceptron Learning Rule in MATLAB to solve linearly separable classification problems.
We cover key concepts such as:

✅Perceptron architecture and decision boundaries
✅Supervised learning and training sets
✅Weight and bias updates using the Perceptron Learning Rule
✅Convergence and limitations of the Perceptron network
🔻YouTube: third session

Download file and codes (in comment)::
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#MATLAB #MachineLearning #NeuralNetworks #PerceptronLearningRule #AI #ArtificialIntelligence #DeepLearning #DataScience #Programming #Tutorial

🔰Linear Control Training Workshop - Session 3

🔵Learn how to analyze the transient response of control systems using MATLAB in this comprehensive tutorial video. We cover step response, impulse response, ramp response, and response to arbitrary inputs. Discover how to obtain key parameters like rise time, peak time, maximum overshoot, and settling time. We also explore generating 3D plots of response curves. Improve your understanding of control system behavior and master transient response analysis with MATLAB.
🔸Telegram:
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE

#MATLAB #ControlSystems #TransientResponse #StepResponse #ImpulseResponse #RampResponse #RiseTime #PeakTime #Overshoot #SettlingTime #3DPlots #EngineeringTutorial #ControlTheory

⚜️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)::
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#MATLAB #PCA #PrincipalComponentAnalysis #AnchorGraphs #DimensionalityReduction #MachineLearning #DataScience #Tutorial #Eigenvectors #Covariance #DataVisualization #Code #Programming

🔰Linear Control Training Workshop - Session 2

🔹Partial Fraction Expansion: Learn how to use the residue() command to easily perform partial fraction expansion on transfer functions. See examples of expanding proper and improper rational functions.
🔸Transforming Mathematical Models: Discover how to convert between different representations of dynamic systems using commands like tf2ss, ss2tf, zp2tf, etc. Examples show conversions between transfer functions, state-space models, pole-zero form, and discrete-time systems.
🔹Block Diagram Modeling: Master the techniques for representing interconnected systems with transfer function or state-space blocks. Learn the MATLAB syntax for series, parallel, and feedback connections. See how to extract the overall transfer function or state-space model.
🔸Telegram:
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE
#MATLAB #ControlSystems #DynamicSystems #TransferFunctions #StateSpace #BlockDiagrams #ModelConversion #PartialFractions #MATLABTutorial #ModelingAndAnalysis

⚜️Neural network course session two::
2️⃣Neuron Model and Network

🔵Explore neuron models and neural network architectures in this comprehensive session. Understand the mathematical foundations of these computational models. Study single and multiple-input neuron models, transfer functions, and how neurons form network building blocks. Discover single-layer, multi-layer, and recurrent network architectures designed for various problem complexities. Learn about feedback loops enabling temporal behavior in recurrent networks.

✅Neuron Model
✅Transfer Functions
✅Network Architectures
✅Recurrent Networks
🔻YouTube: third session
https://youtu.be/DvaMtUP095Q
Download file and codes (in comment)::
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#NeuralNetworks #NeuronModels #NetworkArchitectures #ArtificialNeurons #TransferFunctions #SingleLayerNetworks #MultiLayerNetworks #RecurrentNetworks #DeepLearning #NeuralNetworkDesign #ComputationalModels #MATLAB #MATLABCourse #NeuralNetworkCourse

🔰Linear Control Training Workshop - Session 1

🟢This video covers the first session of a comprehensive linear control training workshop. Linear control theory is fundamental to understanding and designing control systems in various engineering applications.

In this session, you'll learn the basics of linear control, including:
🔹 Introduction to control systems and their components
🔸 Modeling linear systems using transfer functions and state-space representations
🔹Analyzing system stability and performance using tools like root locus and frequency response methods
🔸Basic control design techniques like PID control
Whether you're a student, engineer, or professional in the field of control systems, this video will provide a solid foundation for understanding linear control concepts and techniques.
🔹Telegram:
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE

#LinearControl #ControlSystems #ControlTheory #SystemModeling #SystemStability #ControlDesign #EngineeringEducation

کورس LLM دانشگاه شریف

این ترم دانشکده کامپیوتر شریف کورسی رو در مقطع تحصیلات تکمیلی با موضوع LLM‌ها (مدل‌های‌زبانی بزرگ) و مسائل مربوط به اونها با تدریس مشترک دکتر سلیمانی، دکتر عسگری و دکتر رهبان ارائه کرده. خوبی این کورس اینه که به صورت جامع و کاملی انواع مباحث موردنیاز رو بحث کرده (از معرفی معماری ترنسفورمری گرفته تا فرآیند‌های جمع آوری داده و روش‌های PEFT و ...) از همه اینها مهمتر، فیلم‌ها و تمرین‌های این کورس هم به صورت پابلیک در لینک درس قرار می‌گیرن. از دست ندید.

لینک کورس:
sharif-llm.ir

لینک ویدیوها:
https://ocw.sharif.edu/course/id/524


🔹Telegram:
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE

#course
#coach

🔺Fuzzy System Optimization Step-by-Step: Enhancing Interpolation with Genetic Algorithms🔻:

✳️In this detailed tutorial, we dive into the complex world of fuzzy system optimization using a genetic algorithm. Watch as we methodically enhance a fuzzy model to predict outputs more accurately through successive generations of optimization. Starting with the basics, the video explains the generation of membership functions for inputs and outputs and how they evolve through various iterations to minimize error.

🔻YouTube: https://youtu.be/KXM-diXXEyE
🔹Telegram:
🆔Channel: @MATLAB_House

🆔Group:@MATLABHOUSE

#FuzzyLogic #GeneticAlgorithm #SystemOptimization #MachineLearning #ArtificialIntelligence #DataScience #EngineeringTutorials #MATLAB #OptimizationTechniques #AlgorithmDevelopment #MATLAB_2024

⚜️Neural network course session one::
1️⃣Introduction to Neural Networks

🔵This video provides an introduction to the fascinating world of neural networks. We explore the biological inspiration behind artificial neural networks, drawing parallels between the human brain and these computational models. Key topics covered include:

✅History of neural networks and major milestones
✅Comparison of biological and artificial neuron speeds
✅Loss of neurons with age and neuroplasticity
✅How the brain processes information and learns
✅Applications of neural networks across diverse fields
✅Further reading resources on neural network fundamentals
To see the next meeting earlier, visit the YouTube
🔻YouTube: second session
https://youtu.be/JtBebQ2CJKs

Download file and codes (in comment)::
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE


#NeuralNetworks #ArtificialIntelligence #MachineLearning #Neurons #BrainInspired #Neuroplasticity #DeepLearning #AI #AINeuralNetworks #ComputationalNeuroscience #NeuralNetworkApplications

❇️Major Updates:
- Computer Vision Toolbox: Deploy YOLOX object detection; conduct team-based labeling; perform real-time visual SLAM.
- Deep Learning Toolbox: Support architectures such as transformers; import and co-simulate PyTorch and TensorFlow models.
- GPU Coder: Generate generic CUDA for deep learning; use single memory manager and profile code for MEX code generation.
- Instrument Control Toolbox: Use the Instrument Explorer app to manage devices with IVI and VXIplug&play drivers without writing code.
- Satellite Communications Toolbox: Model multiplatform scenarios and perform visibility and communications link analyses on them.
- UAV Toolbox: Design and deploy flight controller for a vertical take-off and landing (VTOL) UAV with PX4 hardware-in-the-loop simulation; interface with PX4 Cube Orange Plus and Pixhawk 6c autopilots.

❇️Transitions:
- Simulink 3D Animation: Simulate and visualize dynamic systems in Unreal Engine 5.1 with new prebuilt scenes, actors, and sensors.
- SoC Blockset: Prototype and test on SDR and vision hardware with SoC Blockset Support Package for Xilinx Devices.

❇️MATLAB and Simulink Updates:
- Editor Spell Checker: Check spelling in text and comments in MATLAB code files.
- Simulink Editor: Preserve signal line shape when moving and resizing blocks.

❇️MATLAB:
- Local Functions: Define functions anywhere in scripts and live scripts.
- Python Interface: Convert between MATLAB tables and Python Pandas DataFrames.
- Python Interface: Interactively run Python code with Run Python Live Editor task.
- REST Function Service: Call MATLAB functions from any local or remote client program using REST.
- Secrets in MATLAB Vault: Remove sensitive information from code.
- ode Object: Solve ODEs and perform sensitivity analysis using SUNDIALS solvers.

❇️Simulink:
- Simulink Solver: Use local solvers for components with faster dynamics.
- Simulation Object: Control the execution and tune parameter values of scripted simulations.
- MATLAB Apps: Create a custom app that interfaces with a Simulink model using MATLAB App Designer.

❇️Support Packages
- 6G Exploration Library for 5G Toolbox
- Audio Toolbox Interface for SpeechBrain Library
- Computer Vision Toolbox Model for Pose Mask R-CNN 6-DOF Object Pose Estimation
- Databricks ODBC Driver
- Embedded Coder Support Package for Infineon AURIX TC3x Processors
- Lidar Toolbox Model for RandLA-Net Semantic Segmentation
- Lidar Toolbox Support Package for Hokuyo Lidar Sensors
- MariaDB ODBC Driver
- PostgreSQL ODBC Driver

🆔 @MATLAB_House

@MATLABHOUSE

#matlab_2024

🟢R2024a Release Highlights🟢
#MATLAB

🆔 @MATLAB_House

@MATLABHOUSE

❇️Bacterial Foraging Optimization (BSO) ❇️

The text describes a 2D optimization problem aiming to minimize the distance between a position (x1, x2) and the target point (1, 2), with the optimal solution being (1, 2) where the fitness value is zero. It introduces the Bacterial Swarm Optimization (BSO) algorithm, a heuristic method inspired by bacterial foraging behavior. The algorithm operates through a population of individuals that navigate the search space to find the optimal solution based on fitness values and probabilistic rules. It adapts step size and swim length for a balance between exploration and exploitation, and uses elimination-dispersal events to avoid local optima. The algorithm's effectiveness depends on parameter selection and the problem's nature.
🔻YouTube: https://youtu.be/XvQw0RALeTo
🔹Telegram:
🆔 @MATLAB_House

#BSO #algorithm #heuristic #optimization #search_space #bacteria #population #exploration #exploitation
@MATLABHOUSE

❇️Mastering Optimization with Slime Mould Algorithm: A MATLAB Tutorial
Dive into our MATLAB tutorial on the Slime Mould Algorithm (SMA) for stochastic optimization. Learn how SMA, inspired by nature, addresses complex optimization problems. This video covers SMA's basics, its MATLAB implementation, and showcases its effectiveness with visualizations and examples, catering to both beginners and experts. Ideal for researchers, students, and enthusiasts in computational intelligence, this tutorial is designed to enrich your optimization knowledge and spark innovation.
🔻YouTube: https://youtu.be/FqDkJSRGBiU
🔹Telegram:
🆔 @MATLAB_House

@MATLABHOUSE

#SlimeMouldAlgorithm #OptimizationTutorial #MATLABCoding #StochasticOptimization #AlgorithmVisualisation #ComputationalIntelligence #MATLABTutorial #EngineeringEducation #ScienceAndTechnology #ResearchInnovation