1. Dr.-Ing. Erwin Sitompul President University Lecture 1 Introduction to Neural Networks and Fuzzy Logic President UniversityErwin SitompulNNFL 1/1 http://zitompul.wordpress.com

2. President UniversityErwin SitompulNNFL 1/2 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. Textbooks Introduction to Neural Networks and Fuzzy Logic

3. President UniversityErwin SitompulNNFL 1/3 Grade Policy Introduction to Neural Networks and Fuzzy Logic Final Grade = 20% Homework + 20% Quizzes + 30% Midterm Exam + 30% Final Exam + Extra Points Homeworks will be given in fairly regular basis. The average of homework grades contributes 20% of final grade. Written homeworks are to be submitted on A4 papers, otherwise they will not be graded. Homeworks must be submitted on time. If you submit late, < 10 min.  No penalty 10 – 60 min.  –20 points > 60 min.  –40 points There will be 3 quizzes. Only the best 2 will be counted. The average of quiz grades contributes 20% of final grade. Midterm and final exam schedule will be announced in time. Make up of quizzes and exams will be held one week after the schedule of the respective quizzes and exams.

4. President UniversityErwin SitompulNNFL 1/4 Grade Policy Introduction to Neural Networks and Fuzzy Logic The score of a make up quiz or exam, upon discretion, can be multiplied by 0.9 (the maximum score for a make up is then 90). Extra points will be given if you solve a problem in front of the class. You will earn 1, 2, or 3 points. Lecture slides can be copied during class session. It is also available on internet. Please check the course homepage regularly. http://zitompul.wordpress.com Heading of Written Homework Papers (Required)

5. President UniversityErwin SitompulNNFL 1/5 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. IntroductionNeural Networks

6. President UniversityErwin SitompulNNFL 1/6 Experimental Modeling Experimental modeling consists of three steps: 1.The choice of model class 2.The choice of model structures (number of parameters, model order, time delay) 3.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. IntroductionNeural Networks

7. President UniversityErwin SitompulNNFL 1/7 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: 1.Knowledge is acquired by the network from its environment through a learning process. 2.Interneuron connection strengths, known as synaptic weights, are used to store the acquired knowledge. IntroductionNeural Networks

8. President UniversityErwin SitompulNNFL 1/8 Biological and Artificial Neuron Structure of Biological neuron Structure of Artificial neuron Activation function IntroductionNeural Networks

9. President UniversityErwin SitompulNNFL 1/9 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. Linear function Tangent sigmoid function Logarithmic sigmoid function Radial basis function IntroductionNeural Networks

10. President UniversityErwin SitompulNNFL 1/10 Network Architectures Single layer feedforward network (Single layer perceptron) Input layer Output layer Multilayer feedforward network (Multilayer perceptron) Input layer Output layer Hidden layer IntroductionNeural Networks

11. President UniversityErwin SitompulNNFL 1/11 Network Architectures Diagonal recurrent networks Input layer Output layer Hidden layer Input layer Output layer Hidden layer Delay element in a recurrent network Fully recurrent networks IntroductionNeural Networks

12. President UniversityErwin SitompulNNFL 1/12 Network Architectures Elman’s recurrent networksJordan’s recurrent networks IntroductionNeural Networks

13. President UniversityErwin SitompulNNFL 1/13 Preparation Assignment IntroductionNeural Networks Ensure yourself to install Matlab 7 in your computer, along with Matlab Simulink, Control System Toolbox, and Fuzzy Logic Toolbox. Quizzes, Midterm Exam, and Final Exam will be computer-based.