2. Introduction to Neural Networks and Fuzzy Logic
Textbooks
Textbook:
“Neural Networks. A Comprehensive Foundation”, 2nd Edition, Simon Haykin, Prentice Hall, 1999.
“Fuzzy Systems Theory and Its Application”, Toshiro Terano et. al., Academic Press, 1992.

3. Grade Policy Final Grade = Academic + 9 Values + Extra Points
Introduction to Neural Networks and Fuzzy Logic
Grade Policy
Final Grade = Academic + 9 Values + Extra Points
Academic = 6% Notes + 16% Homework + 16% Quizzes % Midterm Exam + 26% Final Exam
9 Values = 6% Peer Assessment + 4% Lecturer Assessment
Your handwritten note will be collected after Quiz 3, and given back to you on the next class.
Handwritten note contributes 6% of final grade.
Homeworks will be given in fairly regular basis. The average of homework grades contributes 16% of final grade.
Written homeworks are to be submitted on A4 papers, otherwise they will not be graded.
Homeworks must be submitted on time, on the day of the next lecture, 10 minutes after the class starts. Late submission will be penalized by point deduction of –10·n, where n is the total number of lateness made.

4. Introduction to Neural Networks and Fuzzy Logic
Grade Policy
There will be 3 quizzes. Only the best 2 will be counted. The average of quiz grades contributes 16% of final grade.
Make up of quizzes must be held within one week after the schedule of the respective quiz.
Midterm exam (26%) and final exam (26%) follow the schedule released by AAB (Academic Administration Bureau).
 Date of the lecture when the homework is issued
Heading of Written Homework Papers (Required)

5. Introduction to Neural Networks and Fuzzy Logic
Grade Policy
Extra points will be given if you solve a problem in front of the class. You will earn 1 or 2.
Lecture slides can be copied during class session. It is also available on internet. Please check the course homepage regularly.
The use of internet for any purpose during class sessions is strictly forbidden.

6. Introduction to Neural Networks
Validation:
Generally, means confirming that a product or service meets the needs of its users.
Testing whether the mathematical model is good enough or not to describe the empirical phenomenon.

7. Experimental Modeling
Neural Networks
Introduction
Experimental Modeling
Experimental modeling consists of three steps:
The choice of model class
The choice of model structures (number of parameters, model order, time delay)
The calculation of the parameters and time delay.
The model may be chosen to be linear, nonlinear, or multi locally-linear.
A-priori (prior, previous) knowledge of the system to be modeled is required in most cases.
Artificial Neural Networks (or simply Neural Networks) offers a general solution for experimental modeling.

8. Experimental Modeling Using Neural Networks
Introduction
Experimental Modeling Using Neural Networks
A neural network is a massively-parallel distributed processor made up of simple processing unit, which has natural propensity for storing experiential knowledge and making it available for use.
It resembles the brain in two respects:
Knowledge is acquired by the network from its environment through a learning process.
Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge.

9. Biological and Artificial Neuron
Neural Networks
Introduction
Biological and Artificial Neuron
Structure of Biological neuron
Activation function
Structure of Artificial neuron

10. Neural Networks
Introduction
Activation Function
Any continuous (differentiable) function can be used as an activation function in a neural network.
The nonlinear behavior of the neural networks is inherited from the used nonlinear activation functions.
Tangent
sigmoid
function
Logarithmic
sigmoid
function
Linear
function
Radial basis
function

11. Network Architectures
Neural Networks
Introduction
Network Architectures
Single layer feedforward network
(Single layer perceptron)
Input
layer
Output
layer
Multilayer feedforward network
(Multilayer perceptron)
Input
layer
Hidden
layer
Output
layer

12. Network Architectures
Neural Networks
Introduction
Network Architectures
Diagonal recurrent networks
Fully recurrent networks
Input
layer
Hidden
layer
Output
layer
Input
layer
Hidden
layer
Output
layer
Delay element in a recurrent network

13. Network Architectures
Neural Networks
Introduction
Network Architectures
Elman’s recurrent networks
Jordan’s recurrent networks

14. Preparation Assignment
Neural Networks
Introduction
Preparation Assignment
Ensure yourself to install Matlab 7 or newer in your computer, along with Matlab Simulink, Control System Toolbox, and Fuzzy Logic Toolbox.
Quizzes, Midterm Exam, and Final Exam will be computer-based.

15. Homework 1A This is an individual work.
Neural Networks
Introduction
Homework 1A
This is an individual work.
Conduct a literature research and prepare a short PowerPoint presentation about the applications and implementations of neural networks in area of:
Manufacturing (Tamara)
Robotics (Andre)
Medics (Keanu)
Military (Fikri)
Administration/ Data Mining (Rudy)
Sports/ Entertainment (Fadhilla)
Recognition/ Identification (Alief)
E-Commerce (Maulidya)
Show the structure of the neural networks used in your presentation
You will be given 15 minutes time for presentation on Monday, 22 January 2018.