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 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

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

⚜️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

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

🔺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
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#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

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

❇️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

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

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


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

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

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.
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This video has Persian subtitles and noise removal and quality enhancement.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_3

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.
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This video has Persian subtitles and noise removal and quality enhancement.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #geneticprogramming #AI #computerscience #Part_2

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.
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This video has Persian subtitles and noise removal and quality enhancement.
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#VirtualNetworks #NetworkOptimization #EvolutionaryAlgorithms #GeneticProgramming #ProblemSolving #ArtificialIntelligence #MachineLearning #ComputationalIntelligence #Networking #Part_1

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.

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https://www.youtube.com/watch?v=eN9qZ-dOskM

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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

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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

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#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