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.

Introduction to Neural Networks and Fuzzy Logic Grade Policy 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, 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. There will be 3 quizzes. Only the best 2 will be counted. The average of quiz grades contributes 20% of final grade.

Introduction to Neural Networks and Fuzzy Logic Grade Policy Midterm and final exams follow the schedule released by AAB (Academic Administration Bureau). Make up of quizzes must be held within one week after the schedule of the respective quiz. Make up for mid exam and final exam must be requested directly to AAB. Heading of Written Homework Papers (Required)

Introduction to Neural Networks and Fuzzy Logic Grade Policy In order to maintain the integrity, 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 or 2. Lecture slides can be copied during class session. It is also available on internet. Please check the course homepage regularly. http://zitompul.wordpress.com The use of internet for any purpose during class sessions is strictly forbidden.

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.

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.

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.

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

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

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

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

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

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.

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: Manufacturing process. Robotics field Medical field You will be given 15 minutes time for presentation on Tuesday, 13.01.2015.