Neural Networks and Fuzzy Logic (630514) (Short Syllabus )

Lectures adapted from the following books :

Neural Network Design (2nd Edition), Martin T. Hagan and others, 2014 (textbook)

Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Michael Negnevitsky, Addison Wesley, 2005(textbook).

A Brief Introduction to Neural Networks, David Kriesel, 2005

Fuzzy Control and Fuzzy System. By: Witold Pedrycz.Research Studies Press Ltd.2^ndd edition 1996

Introduction to Fuzzy Logic using MATLAB, S. N. Sivanandam, and others, 2007, Springer

Fausett, Laurene. Fundamentals of Neural Networks: Architectures, Algorithms and Applications. Prentice Hall, Englewood Cliffs, NJ, 1994.

Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering, Nikola K. Kasabov, 1998, MIT Press.

Neural Networks:A Comprehensive Study By: Simon Hyken. Macmillan Colledge Publishing, Inc.1996

Matlab Home page: http://www.mathworks.com

Lectures

Neural Networks

Lecture 1 - Neural Network Definitions and Concepts (pdf)

Lecture 2 - Components of ANN and MATLAB representation (pdf)

Lecture 3 - MATLAB representation of neural network (pdf)

Lecture 4 - Solving simple pattern recognition problem using ANN (pdf)

Lecture 5 - Multi-Layer Feedforward Neural Networks using matlab Part 1 (pdf)

Lecture 6 - Multi-Layer Feedforward Neural Networks using matlab Part 2 (pdf)

--------------Function Fitting (one input) : Matlab Code (FFMLP_FF_1_Input. m)

--------------Function Fitting (two inputs) : Matlab Code (FFMLP_FF_2_Inputs. m)

Lecture 7 - Perceptrons and Multi-Layer FF NN using matlab Part 3 (pdf)

--------------linearly-separable classification : Matlab Code (AND_Gate.m)

--------------Pattern Classification : Matlab Code (Image_classification.m)

Lecture 8 - Supervised Learning in Neural Networks -(Part 1) (pdf)

Lecture 9 - Supervised Learning in Neural Networks -(Part 2) (pdf) -----new version

Lecure 9A - http://scholastictutors.webs.com/Scholastic-Book-NeuralNetworks-Part03-2013-10-22.pdf

https://www.youtube.com/watch?v=I2I5ztVfUSE

Lecture 10 -Supervised Learning in Neural Networks -(Part 3) using matlab (pdf)

Lecture 11 - Supervised Learning _Hopfield Networks -(Part 4) (pdf)

Lecture 12 -Supervised Learning _Hopfield Networks -(Part 5) (pdf)

Lecture 13 -Supervised Learning _Bidirectional associative memory(BAM) (pdf)

Lecture 14_ Associative Neural Networks using Matlab (pdf)

--------------Associative Neural Networks (Matlab Code) :

---Associative_Example_1.m-----Associative_Example_2.m-----Associative_Example_3.m

---Associative_Example_4.m----Associative_Example_5.m----- Associative_Example_6.m

------------------------------ Associative_Example_7.m

Lecture 15_Self-Organizing Maps (Kohonen Maps) (pdf)

Lecture 16 _Self-organizing map using matlab (pdf)

--------------Self-Organizing Maps (Matlab Code) :

------- Example 1 (SOM1.m) --------Example 2 (SOM2.m) --------Example 3 (SOM3.m)

Fuzzy Logic

Lecture 17-Fuzzy Expert Systems (pdf)

Lecture 18_Different Types of Membership Functions (pdf)

Lecture 19_Operations and rules of fuzzy sets (pdf)

Lecture 20_Mamdani-Type Fuzzy Inference Process (PP 106-112 ) from the book:
Artificial Intelligence: A Guide to Intelligent Systems (2nd Edition), Michael Negnevitsky, Addison Wesley, 2005.

Lecture 21_Sugeno Fuzzy Models (pdf)

Lecture 22- Build Mamdani Systems (GUI) (Click here)