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Hidden Markov Model (HMM)

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Hidden Markov Model (HMM)  VIDEO LINK:  https://youtu.be/YIGCWNG8BIA A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. Video Chapters: HMM in Artificial Intelligence 00:00 Introduction 00:31 Statistical Model 00:54 HMM Examples 02:30 HMM 03:10 HMM Components 05:23 Viterbi Algorithm 06:23 HMM Applications 06:38 HMM Problems 07:28 HMM in Handwriting Recognition 11:20 Conclusion  HMM COMPONENTS A Hidden Markov Model (HMM) is a statistical model in which the system has hidden states that cannot be directly observed, but produce observable outputs. It is based on the Markov property, meaning the next state depends only on the current state. An HMM consists of states, observations, transition probabilities, emission probabilities, and initial probabilities. It is commonly used in a...
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Harmony Search Algorithm Numerical Example | Step-By-Step |~xRay Pixy

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Harmony Search Algorithm


Video Chapters: Introduction: 00:00 Harmony Search Algorithm: 01:00 Harmony Search Algorithm Example 1: 04:00 Harmony Search Algorithm Numerical Example 2: 07:26 Harmony Search Algorithm Numerical Example 3: 11:36 Conclusion: 15:00

How does harmony search algorithm work?

Harmony Search Algorithm (HSA) is introduced by Zong Woo Geem and Joong Hoon Kim in 2001. Harmony Search is a music inspired optimization algorithm. Harmony Search Algorithm is basically inspired by the Music Harmony.

Music Harmony refers to the relationship between sound waves coming either from musical instruments or human voices. It is the process by which individual sounds are joined together simultaneously. It is the combination of sound pitches in the music. Pitch is an aspect of sound that we can hear. Through pitch we can check weather sound is High or Low than other musical sound.

Harmony Search Algorithm Main Rules: Musician has 3 choices
  1. Play Famous piece of Music (i.e., known).
  2. Play something similar to the famous piece of Music.
  3. Compose New Music.
Harmony Search Algorithm Steps:
  1. Initialize algorithm parameters such as Harmony Memory, Maximum Iterations, Pitch Adjustment Rate, Harmony Adjustment Rate and Band Width.
  2. Construct initial harmony randomly.
  3. Select Best and Worst Harmony in the harmony memory.
  4. Check While (current iteration <= Maximum Iteration)
  5. Harmony Memory Generation.
  6. Pitch Adjustment.
  7. Update Harmony [harmony replacement after comparison).
  8. Return best harmony as optimal solution.

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