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Markov Chains || Step-By-Step || ~xRay Pixy

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Learn Markov Chains step-by-step using real-life examples. Click Here   Video Link Video Chapters: Markov Chains 00:00 Introduction 00:19 Topics Covered 01:49 Markov Chains Applications 02:04 Markov Property 03:18 Example 1 03:54 States, State Space, Transition Probabilities 06:17 Transition Matrix 08:17 Example 02 09:17 Example 03 10:26 Example 04 12:25 Example 05 14:16 Example 06 16:49 Example 07 18:11 Example 08 24:56 Conclusion In computer science, Markov problems are typically associated with Markov processes or Markov models . These are related to topics involving stochastic processes and probabilistic systems where future states depend only on the current state, not on the sequence of states that preceded it. Artificial Intelligence (AI): Markov Decision Processes (MDP): Used in decision-making problems, especially in reinforcement learning. Hidden Markov Models (HMM): Widely used in speech recognition, handwriting recognition, and natural language processing. Machine Le...
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Particle Swarm Optimization (PSO)

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Particle Swarm Optimization (PSO) is a population-based stochastic search algorithm. PSO is inspired by the Social Behavior of Birds flocking. PSO is a computational method that Optimizes a problem. PSO searches for Optima by updating generations. It is popular is an intelligent metaheuristic algorithm. In Particle Swarm Optimization the solution of the problem is represented using Particles. [Flocking birds are replaced with particles for algorithm simplicity]. Objective Function is used for the performance evaluation for each particle / agent in the current population. After a number of iterations agents / particles will find out optimal solution in the search space.

Q. What is PSO? A. PSO is a computational method that Optimizes a problem.

Q. How PSO will optimize? A. By Improving a Candidate Solution. Q. How PSO Solve Problems? A. PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). Local and global optimal solutions are used to update particle position in each iteration.

1.) The population of Candidate Solutions (i.e., Particles)

2.) The movement of Each particle is influenced by its Local Best Known Position and global best position[guided towards the best-known position in the search space].

3.) Move the swarm towards the best solution.

PSO Advantages PSO is Easy to Implement. Only a few parameters are used in PSO. Successfully applied in Function Optima, ANN training, and fuzzy control system.

PSO solved problems by having a Population (called Swarms) of Candidate Solutions (Particles). PSO Advantages PSO is Easy to Implement. Only a few parameters are used in PSO. Successfully applied in Function Optima, ANN training, and fuzzy control system.














#Metaheuristic #Algorithms
Meta-heuristic Algorithms
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Comments

  1. Rajesh9 May 2021 at 22:35

    Dear Ma'am,

    I modified the cost function like

    function z = Raj(x)

    z = (x.^2+2.*x);

    end

    but coding not working

    Error in the
    BestCosts(it) = GlobalBest.Cost;

    kindly help me in this regard

    Kindly send me ur mail id so i can forward code

    ReplyDelete
    Replies
    1. Ritika Saini29 May 2021 at 09:37

      You can contact me on twitter. Change certain paraments according to your function.

      Delete

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