Fuzzy Logic And Neural Networks in 2020
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.
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