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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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Optimization Engineering - Design Optimization

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 [ CAN Design Optimization using Teaching Learning Based Optimization Algorithm]


PROBLEM STATEMENT: Design a Can to Hold 800ml Liquid.

OBJECTIVE: Minimize the CAN Manufacturing Cost, Minimize Amount of Sheet Metal Required. 

CONSTRAINTS: For Diameter, It should be no greater than 16 cm and no less than 4.0 cm, For Height, It should be no more than 36 cm and no less than 16 cm.
                                                    4.0 <= Diameter <= 16; cm
                                                    16<= Height <= 36; cm

Constraints to HOLD 800ml Liquid Capacity.
OBJECTIVE FUNCTION: COST Function Used to Solve this problem:
RESULT: AFTER OPTIMIZATION USING TEACHING LEARNING BASED OPTIMIZATION ALGORITHM

Optimal Diameter 4.3679
Optimal Height 34.0069
Best Cost = 207.24

Teaching Learning Based Optimization Algorithm

 | TLBO Numerical Example |

Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example.

Teaching Learning Based Optimization Algorithm is based on the effect of Teacher on the Learners in the class. Teaching Learning Based Optimization Algorithm is basically inspired by the behavior of learners in the classroom. In Teaching Learning Based Optimization Algorithm 2 main procedure are followed:

  1. Teaching Phase: Learners study from the Teacher.
  2. Learner Phase: Learners can interact with each other and they can randomly interact with each other.

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PSO Python Code || Particle Swarm Optimization in Python || ~xRay Pixy

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