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Nash Equilibrium In Game Theory ~xRay Pixy

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 Video Link  CLICK HERE... Learn Nash Equilibrium In Game Theory Step-By-Step Using Examples. Video Chapters: Nash Equilibrium  00:00 Introduction 00:19 Topics Covered 00:33 Nash Equilibrium  01:55 Example 1  02:30 Example 2 04:46 Game Core Elements 06:41 Types of Game Strategies 06:55  Prisoner’s Dilemma  07:17  Prisoner’s Dilemma Example 3 09:16 Dominated Strategy  10:56 Applications 11:34 Conclusion The Nash Equilibrium is a concept in game theory that describes a situation where no player can benefit by changing their strategy while the other players keep their strategies unchanged.  No player can increase their payoff by changing their choice alone while others keep theirs the same. Example : If Chrysler, Ford, and GM each choose their production levels so that no company can make more money by changing their choice, it’s a Nash Equilibrium Prisoner’s Dilemma : Two criminals are arrested and interrogated separately. Each has two ...
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Quantum Cat Swarm Optimization Algorithm || Step-By-Step || ~xRay Pixy

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Learn Quantum Cat Swarm Optimization Algorithm Step-By-Step using Example Video Chapters: QCSO 00:00 Introduction 01:29 Cat Swarm Optimization 06:34 Quantum Cat Swarm Optimization 08:46 Quantum Computing Principles 12:12  What is Quantum Bit (Qubit)? 12:46 Quantum Population Initialization 16:26 QCSO Advantages 17:05 QCSO Applications 17:20 Conclusion ns 17:20 Conclusion
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Soft Computing - Neural Networks || Module 2 || ~xRay Pixy

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Video Chapters: Soft Computing Module 2 - Neural Networks 00:00 Introduction 00:39 Topics Covered in Video 01:19 Neural Network Basics 05:12 Problem 01 - Calculate Net Input to Output Neuron 06:33 Artificial Neural Network Terminologies 06:54 Weights 07:59 Bias 08:13 Threshold 08:33 Learning Rate 08:49 Momemtum Factor 09:03 Problem 02 - Calculate Output of Neuron (Y) using Activation Function 09:30 Activation Function 12:42 Problem 02 - Solution 15:25 Neural Network Types 15:38 Simple Neural Network 16:08 Single Feedforward Neural Network 16:41 Multilayer Feedforward Neural Network 17:21 Single Layer Recurrent Network 17:50 Multilayer Recurrent Network 17:52 Perceptron 21:57 Multilayer Perceptron 22:15 Adaline -Its Training and Capabilities 22:59 Conclusion
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Find Maxima of Function using PSO Method || Numerical Example || ~xRay Pixy

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Find the maximum value for the objective function using Particle Swarm Optimization Step-By-Step. Video Chapters: Find the Maxima of Function using the PSO Method 00:00 Introduction 02:18 Objective 03:17 Maximization Problem 04:22 Particle Swarm Optimization Steps 05:22 Step 1 - Objective Function 05:30 Step 2 - Position and Velocity Initialization 06:00 Step 3 - Fitness Calculation 07:06 Step 4 - Update Personal Best 07:16 Step 5 - Update Global Best 07:42 Step 6 - Position Update 10:34 Step 7 - Solution Boundary Checking 10:53 Step 8 - New Solution Evaluation 11:31 Step 9 - Update Personal Best 12:12 Step 10 - Update Global Best 13:24 Iteration 2 Start - Position Update 14:45 New Solution Boundary Checking 15:24 New Solution Fitness Calculation 15:48 Update Personal Best 16:32 Update Global Best 17:42 Conclusion Problem: Find the Maxima of the function � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 + 2 x + 11 � ( � ) = � 2 + 2 � + 11 f ( x ) = x 2 in the range -2<=x<=2 using PSO m...
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