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Fuzzy Logic And Neural Networks in 2020

  • Course level: Beginner
  • Last Update 23/06/2021

Description

Fuzzy Logic And Neural Networks

This course will start with a brief introduction to fuzzy sets. The differences between fuzzy sets and crisp sets will be identified. Various terms used in the fuzzy sets and the grammar of fuzzy sets will be discussed, in detail, with the help of some numerical examples. The working principles of the two most popular applications of fuzzy sets, namely fuzzy reasoning, and fuzzy clustering will be explained, and numerical examples will be solved. The fundamentals of neural networks and various learning methods will then be discussed. The principles of multi-layer feed-forward neural network, radial basis function network, self-organizing map, counter-propagation neural network, recurrent neural network, deep learning neural network will be explained with appropriate numerical examples. The method of evolving optimized fuzzy reasoning tools, neural networks will be discussed with the help of some numerical examples. Two popular neuro-fuzzy systems will be explained and numerical examples will be solved. A summary of the course will be given at the end.

Join us at Fuzzy Logic And Neural Networks !!

 

What Will I Learn?

  • Introduction to Fuzzy Sets
  • learn the Applications of Fuzzy Sets
  • Learn the Optimization of Fuzzy Reasoning and Clustering Tool
  • Some Examples of Neural Networks
  • Learn the Optimal Designs of Neural Networks
  • Learn the Concepts of Soft Computing and Expert Systems

Topics for this course

42 Lessons

Fuzzy Logic & Neural Networks

Lecture 01: Introduction to Fuzzy Sets00:00:00
Lecture 02: Introduction to Fuzzy Sets (Contd.)00:00:00
Lecture 03: Introduction to Fuzzy Sets (Contd.)00:00:00
Lecture 04: Introduction to Fuzzy Sets (Contd.)00:00:00
Lecture 05: Introduction to Fuzzy Sets (Contd.)00:00:00
Lecture 06: Introduction to Fuzzy Sets (Contd.)00:00:00
Lecture 07: Applications of Fuzzy Sets00:00:00
Lecture 08: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 09: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 10: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 11: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 12 : Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 13 : Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 14: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 15: Applications of Fuzzy Sets (Contd.)00:00:00
Lecture 16: Applications of Fuzzy Sets (Contd.)00:00:00
Lectuer 17: Optimizationof Fuzzy Reasoning and Clustering Tool00:00:00
Lecture 18: Optimizationof Fuzzy Reasoning and Clustering Tool (Contd.)00:00:00
Lecture 19: Optimizationof Fuzzy Reasoning and Clustering Tool (Contd.)00:00:00
Lecture 20: Optimizationof Fuzzy Reasoning and Clustering Tool (Contd.)00:00:00
Lecture 21: Some Examples of Neural Networks00:00:00
Lecture 22: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 23: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 24: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 25: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 26: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 27: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 28: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 29: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 30: Some Examples of Neural Networks (Contd.)00:00:00
Lecture 31: Optimal Designs of Neural Networks00:00:00
Lecture 32: Optimal Designs of Neural Networks (Contd.)00:00:00
Lecture 33: Neuro-Fuzzy System00:00:00
Lecture 34: Neuro-Fuzzy System (Contd.)00:00:00
Lecture 35: Neuro-Fuzzy System (Contd.)00:00:00
Lecture 36: Neuro-Fuzzy System (Contd.)00:00:00
Lecture 37: Concepts of Soft Computing and Expert Systems00:00:00
Lecture 38: Concepts of Soft Computing and Expert Systems (Contd.)00:00:00
Lecture 39: A Few Applications00:00:00
Lecture 40: A Few Applications (Contd.)00:00:00
Lecture 41: A Few Applications (Contd.)00:00:00
Lecture 42: A Few Applications (Contd.)00:00:00
Fuzzy Logic And Neural Networks
Free

Enrolment validity: Lifetime

Requirements

  • Non